id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 50 116 |
|---|---|---|
f1a9f7863715-26 | {'action': 'on_llm_end', 'token_usage_prompt_tokens': 242, 'token_usage_completion_tokens': 28, 'token_usage_total_tokens': 270, 'model_name': 'text-davinci-003', 'step': 8, 'starts': 5, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_st... | https:///python.langchain.com/en/latest/ecosystem/clearml_tracking.html |
f1a9f7863715-27 | I need to find out who Bryan Adams is married to.
Action: Search
Action Input: "Who is Bryan Adams married to"{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who is Bryan Adams married to', 'log': ' I need to find out who Bryan Adams is married to.\nAction: Search\nAction Input: "Who is Bryan Adams marri... | https:///python.langchain.com/en/latest/ecosystem/clearml_tracking.html |
f1a9f7863715-28 | Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...', 'step': 11, 'starts': 7, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_en... | https:///python.langchain.com/en/latest/ecosystem/clearml_tracking.html |
f1a9f7863715-29 | {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 12, 'starts': 8, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have acces... | https:///python.langchain.com/en/latest/ecosystem/clearml_tracking.html |
f1a9f7863715-30 | to the original input question\n\nBegin!\n\nQuestion: Who is the wife of the person who sang summer of 69?\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\nThou... | https:///python.langchain.com/en/latest/ecosystem/clearml_tracking.html |
f1a9f7863715-31 | {'action': 'on_llm_end', 'token_usage_prompt_tokens': 314, 'token_usage_completion_tokens': 18, 'token_usage_total_tokens': 332, 'model_name': 'text-davinci-003', 'step': 13, 'starts': 8, 'ends': 5, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_s... | https:///python.langchain.com/en/latest/ecosystem/clearml_tracking.html |
f1a9f7863715-32 | I now know the final answer.
Final Answer: Bryan Adams has never been married.
{'action': 'on_agent_finish', 'output': 'Bryan Adams has never been married.', 'log': ' I now know the final answer.\nFinal Answer: Bryan Adams has never been married.', 'step': 14, 'starts': 8, 'ends': 6, 'errors': 0, 'text_ctr': 0, 'chain_... | https:///python.langchain.com/en/latest/ecosystem/clearml_tracking.html |
f1a9f7863715-33 | 4 on_llm_start OpenAI 1 1 0 0 0
.. ... ... ... ... ... ... ...
66 on_tool_end NaN 11 7 4 0 0
67 on_llm_start OpenAI 12 8 4 0 0
68 on_llm_end NaN 13 8 ... | https:///python.langchain.com/en/latest/ecosystem/clearml_tracking.html |
f1a9f7863715-34 | 69 1 0 3 ... NaN NaN NaN
70 1 1 3 ... NaN NaN NaN
tool tool_input log \
0 NaN NaN NaN
1 NaN ... | https:///python.langchain.com/en/latest/ecosystem/clearml_tracking.html |
f1a9f7863715-35 | 0 2 Answer the following questions as best you can... OpenAI
1 7 Answer the following questions as best you can... OpenAI
2 12 Answer the following questions as best you can... OpenAI
output_step output \
0 3 I n... | https:///python.langchain.com/en/latest/ecosystem/clearml_tracking.html |
f1a9f7863715-36 | 2 3rd and 4th grade 115.70 110.84 49.79
crawford gulpease_index osman
0 0.9 72.7 92.16
1 0.7 74.7 84.20
2 0.7 85.4 83.14
[3 rows x 24 columns]}
Could not update last created model in Task 988bd727b0e94a29a3ac0ee526813545... | https:///python.langchain.com/en/latest/ecosystem/clearml_tracking.html |
2b82ac8374a6-0 | .md
.pdf
Metal
Contents
What is Metal?
Quick start
Metal#
This page covers how to use Metal within LangChain.
What is Metal?#
Metal is a managed retrieval & memory platform built for production. Easily index your data into Metal and run semantic search and retrieval on it.
Quick start#
Get started by creating a Meta... | https:///python.langchain.com/en/latest/ecosystem/metal.html |
6908f86a9652-0 | .md
.pdf
RWKV-4
Contents
Installation and Setup
Usage
RWKV
Model File
Rwkv-4 models -> recommended VRAM
RWKV-4#
This page covers how to use the RWKV-4 wrapper within LangChain.
It is broken into two parts: installation and setup, and then usage with an example.
Installation and Setup#
Install the Python package with ... | https:///python.langchain.com/en/latest/ecosystem/rwkv.html |
6908f86a9652-1 | RWKV VRAM
Model | 8bit | bf16/fp16 | fp32
14B | 16GB | 28GB | >50GB
7B | 8GB | 14GB | 28GB
3B | 2.8GB| 6GB | 12GB
1b5 | 1.3GB| 3GB | 6GB
See the rwkv pip page for more information about strategies, including streaming and cuda support.
previous
Runhouse
next
SearxNG Search API
Contents... | https:///python.langchain.com/en/latest/ecosystem/rwkv.html |
c7a366428011-0 | .md
.pdf
Replicate
Contents
Installation and Setup
Calling a model
Replicate#
This page covers how to run models on Replicate within LangChain.
Installation and Setup#
Create a Replicate account. Get your API key and set it as an environment variable (REPLICATE_API_TOKEN)
Install the Replicate python client with pip ... | https:///python.langchain.com/en/latest/ecosystem/replicate.html |
c7a366428011-1 | And run it:
prompt = """
Answer the following yes/no question by reasoning step by step.
Can a dog drive a car?
"""
llm(prompt)
We can call any Replicate model (not just LLMs) using this syntax. For example, we can call Stable Diffusion:
text2image = Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46e... | https:///python.langchain.com/en/latest/ecosystem/replicate.html |
390663403a74-0 | .ipynb
.pdf
Aim
Aim#
Aim makes it super easy to visualize and debug LangChain executions. Aim tracks inputs and outputs of LLMs and tools, as well as actions of agents.
With Aim, you can easily debug and examine an individual execution:
Additionally, you have the option to compare multiple executions side by side:
Aim ... | https:///python.langchain.com/en/latest/ecosystem/aim_tracking.html |
390663403a74-1 | aim_callback = AimCallbackHandler(
repo=".",
experiment_name="scenario 1: OpenAI LLM",
)
callbacks = [StdOutCallbackHandler(), aim_callback]
llm = OpenAI(temperature=0, callbacks=callbacks)
The flush_tracker function is used to record LangChain assets on Aim. By default, the session is reset rather than being t... | https:///python.langchain.com/en/latest/ecosystem/aim_tracking.html |
390663403a74-2 | )
Scenario 3 The third scenario involves an agent with tools.
from langchain.agents import initialize_agent, load_tools
from langchain.agents import AgentType
# scenario 3 - Agent with Tools
tools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks)
agent = initialize_agent(
tools,
llm,
agent... | https:///python.langchain.com/en/latest/ecosystem/aim_tracking.html |
390663403a74-3 | AnalyticDB
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/ecosystem/aim_tracking.html |
357ca65413db-0 | .md
.pdf
Tair
Contents
Installation and Setup
Wrappers
VectorStore
Tair#
This page covers how to use the Tair ecosystem within LangChain.
Installation and Setup#
Install Tair Python SDK with pip install tair.
Wrappers#
VectorStore#
There exists a wrapper around TairVector, allowing you to use it as a vectorstore,
whe... | https:///python.langchain.com/en/latest/ecosystem/tair.html |
0c686406e479-0 | .md
.pdf
Locally Hosted Setup
Contents
Installation
Environment Setup
Locally Hosted Setup#
This page contains instructions for installing and then setting up the environment to use the locally hosted version of tracing.
Installation#
Ensure you have Docker installed (see Get Docker) and that it’s running.
Install th... | https:///python.langchain.com/en/latest/tracing/local_installation.html |
0c686406e479-1 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/tracing/local_installation.html |
50240fd1997b-0 | .md
.pdf
Cloud Hosted Setup
Contents
Installation
Environment Setup
Cloud Hosted Setup#
We offer a hosted version of tracing at langchainplus.vercel.app. You can use this to view traces from your run without having to run the server locally.
Note: we are currently only offering this to a limited number of users. The ... | https:///python.langchain.com/en/latest/tracing/hosted_installation.html |
50240fd1997b-1 | os.environ["LANGCHAIN_API_KEY"] = "my_api_key" # Don't commit this to your repo! Better to set it in your terminal.
Contents
Installation
Environment Setup
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/tracing/hosted_installation.html |
eb3bdd709883-0 | .ipynb
.pdf
Tracing Walkthrough
Tracing Walkthrough#
There are two recommended ways to trace your LangChains:
Setting the LANGCHAIN_TRACING environment variable to “true”.
Using a context manager with tracing_enabled() to trace a particular block of code.
Note if the environment variable is set, all code will be traced... | https:///python.langchain.com/en/latest/tracing/agent_with_tracing.html |
eb3bdd709883-1 | I need to use a calculator to solve this.
Action: Calculator
Action Input: 2^.123243
Observation: Answer: 1.0891804557407723
Thought: I now know the final answer.
Final Answer: 1.0891804557407723
> Finished chain.
'1.0891804557407723'
# Agent run with tracing using a chat model
agent = initialize_agent(
tools, Chat... | https:///python.langchain.com/en/latest/tracing/agent_with_tracing.html |
eb3bdd709883-2 | Action: Calculator
Action Input: 5 ^ .123243
Observation: Answer: 1.2193914912400514
Thought:I now know the answer to the question.
Final Answer: 1.2193914912400514
> Finished chain.
# Now, we unset the environment variable and use a context manager.
if "LANGCHAIN_TRACING" in os.environ:
del os.environ["LANGCHAIN_... | https:///python.langchain.com/en/latest/tracing/agent_with_tracing.html |
eb3bdd709883-3 | del os.environ["LANGCHAIN_TRACING"]
questions = [f"What is {i} raised to .123 power?" for i in range(1,4)]
# start a background task
task = asyncio.create_task(agent.arun(questions[0])) # this should not be traced
with tracing_enabled() as session:
assert session
tasks = [agent.arun(q) for q in questions[... | https:///python.langchain.com/en/latest/tracing/agent_with_tracing.html |
87147a8e359e-0 | Source code for langchain.text_splitter
"""Functionality for splitting text."""
from __future__ import annotations
import copy
import logging
from abc import ABC, abstractmethod
from typing import (
AbstractSet,
Any,
Callable,
Collection,
Iterable,
List,
Literal,
Optional,
Sequence,
... | https:///python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
87147a8e359e-1 | for chunk in self.split_text(text):
new_doc = Document(
page_content=chunk, metadata=copy.deepcopy(_metadatas[i])
)
documents.append(new_doc)
return documents
[docs] def split_documents(self, documents: List[Document]) -> List[Document]:
... | https:///python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
87147a8e359e-2 | docs.append(doc)
# Keep on popping if:
# - we have a larger chunk than in the chunk overlap
# - or if we still have any chunks and the length is long
while total > self._chunk_overlap or (
total + _len + (separator_l... | https:///python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
87147a8e359e-3 | [docs] @classmethod
def from_tiktoken_encoder(
cls,
encoding_name: str = "gpt2",
model_name: Optional[str] = None,
allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
disallowed_special: Union[Literal["all"], Collection[str]] = "all",
**kwargs: Any,
... | https:///python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
87147a8e359e-4 | """Asynchronously transform a sequence of documents by splitting them."""
raise NotImplementedError
[docs]class CharacterTextSplitter(TextSplitter):
"""Implementation of splitting text that looks at characters."""
def __init__(self, separator: str = "\n\n", **kwargs: Any):
"""Create a new TextSp... | https:///python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
87147a8e359e-5 | enc = tiktoken.encoding_for_model(model_name)
else:
enc = tiktoken.get_encoding(encoding_name)
self._tokenizer = enc
self._allowed_special = allowed_special
self._disallowed_special = disallowed_special
[docs] def split_text(self, text: str) -> List[str]:
"""Split ... | https:///python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
87147a8e359e-6 | # Get appropriate separator to use
separator = self._separators[-1]
for _s in self._separators:
if _s == "":
separator = _s
break
if _s in text:
separator = _s
break
# Now that we have the separator, split th... | https:///python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
87147a8e359e-7 | [docs] def split_text(self, text: str) -> List[str]:
"""Split incoming text and return chunks."""
# First we naively split the large input into a bunch of smaller ones.
splits = self._tokenizer(text)
return self._merge_splits(splits, self._separator)
[docs]class SpacyTextSplitter(Text... | https:///python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
87147a8e359e-8 | # Note the alternative syntax for headings (below) is not handled here
# Heading level 2
# ---------------
# End of code block
"```\n\n",
# Horizontal lines
"\n\n***\n\n",
"\n\n---\n\n",
"\n\n___\n\n",
# Note tha... | https:///python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
87147a8e359e-9 | # Now split by the normal type of lines
" ",
"",
]
super().__init__(separators=separators, **kwargs)
[docs]class PythonCodeTextSplitter(RecursiveCharacterTextSplitter):
"""Attempts to split the text along Python syntax."""
def __init__(self, **kwargs: Any):
"""Ini... | https:///python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
71c2a2faee1f-0 | Source code for langchain.requests
"""Lightweight wrapper around requests library, with async support."""
from contextlib import asynccontextmanager
from typing import Any, AsyncGenerator, Dict, Optional
import aiohttp
import requests
from pydantic import BaseModel, Extra
class Requests(BaseModel):
"""Wrapper aroun... | https:///python.langchain.com/en/latest/_modules/langchain/requests.html |
71c2a2faee1f-1 | def delete(self, url: str, **kwargs: Any) -> requests.Response:
"""DELETE the URL and return the text."""
return requests.delete(url, headers=self.headers, **kwargs)
@asynccontextmanager
async def _arequest(
self, method: str, url: str, **kwargs: Any
) -> AsyncGenerator[aiohttp.Clien... | https:///python.langchain.com/en/latest/_modules/langchain/requests.html |
71c2a2faee1f-2 | """PATCH the URL and return the text asynchronously."""
async with self._arequest("PATCH", url, **kwargs) as response:
yield response
@asynccontextmanager
async def aput(
self, url: str, data: Dict[str, Any], **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
... | https:///python.langchain.com/en/latest/_modules/langchain/requests.html |
71c2a2faee1f-3 | """POST to the URL and return the text."""
return self.requests.post(url, data, **kwargs).text
[docs] def patch(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
"""PATCH the URL and return the text."""
return self.requests.patch(url, data, **kwargs).text
[docs] def put(self, ur... | https:///python.langchain.com/en/latest/_modules/langchain/requests.html |
71c2a2faee1f-4 | """PUT the URL and return the text asynchronously."""
async with self.requests.aput(url, **kwargs) as response:
return await response.text()
[docs] async def adelete(self, url: str, **kwargs: Any) -> str:
"""DELETE the URL and return the text asynchronously."""
async with self.req... | https:///python.langchain.com/en/latest/_modules/langchain/requests.html |
8245ffcd92df-0 | Source code for langchain.document_transformers
"""Transform documents"""
from typing import Any, Callable, List, Sequence
import numpy as np
from pydantic import BaseModel, Field
from langchain.embeddings.base import Embeddings
from langchain.math_utils import cosine_similarity
from langchain.schema import BaseDocumen... | https:///python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
8245ffcd92df-1 | for first_idx, second_idx in redundant_stacked[redundant_sorted]:
if first_idx in included_idxs and second_idx in included_idxs:
# Default to dropping the second document of any highly similar pair.
included_idxs.remove(second_idx)
return list(sorted(included_idxs))
def _get_embeddin... | https:///python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
8245ffcd92df-2 | """Filter down documents."""
stateful_documents = get_stateful_documents(documents)
embedded_documents = _get_embeddings_from_stateful_docs(
self.embeddings, stateful_documents
)
included_idxs = _filter_similar_embeddings(
embedded_documents, self.similarity_fn, s... | https:///python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
6cdb29db0b1a-0 | Source code for langchain.experimental.autonomous_agents.baby_agi.baby_agi
"""BabyAGI agent."""
from collections import deque
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerFo... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
6cdb29db0b1a-1 | print(str(t["task_id"]) + ": " + t["task_name"])
def print_next_task(self, task: Dict) -> None:
print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" + "\033[0m\033[0m")
print(str(task["task_id"]) + ": " + task["task_name"])
def print_task_result(self, result: str) -> None:
print("\033[93m... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
6cdb29db0b1a-2 | next_task_id = int(this_task_id) + 1
response = self.task_prioritization_chain.run(
task_names=", ".join(task_names),
next_task_id=str(next_task_id),
objective=objective,
)
new_tasks = response.split("\n")
prioritized_task_list = []
for task_st... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
6cdb29db0b1a-3 | """Run the agent."""
objective = inputs["objective"]
first_task = inputs.get("first_task", "Make a todo list")
self.add_task({"task_id": 1, "task_name": first_task})
num_iters = 0
while True:
if self.task_list:
self.print_task_list()
# ... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
6cdb29db0b1a-4 | break
return {}
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
vectorstore: VectorStore,
verbose: bool = False,
task_execution_chain: Optional[Chain] = None,
**kwargs: Dict[str, Any],
) -> "BabyAGI":
"""Initialize the BabyAGI Con... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
5198cfa20be5-0 | Source code for langchain.experimental.autonomous_agents.autogpt.agent
from __future__ import annotations
from typing import List, Optional
from pydantic import ValidationError
from langchain.chains.llm import LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.experimental.autonomous_agents.au... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
5198cfa20be5-1 | ai_role: str,
memory: VectorStoreRetriever,
tools: List[BaseTool],
llm: BaseChatModel,
human_in_the_loop: bool = False,
output_parser: Optional[BaseAutoGPTOutputParser] = None,
) -> AutoGPT:
prompt = AutoGPTPrompt(
ai_name=ai_name,
ai_role=ai_r... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
5198cfa20be5-2 | # Get command name and arguments
action = self.output_parser.parse(assistant_reply)
tools = {t.name: t for t in self.tools}
if action.name == FINISH_NAME:
return action.args["response"]
if action.name in tools:
tool = tools[action.name]
... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
66ee35c90655-0 | Source code for langchain.experimental.generative_agents.generative_agent
import re
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from pydantic import BaseModel, Field
from langchain import LLMChain
from langchain.base_language import BaseLanguageModel
from langchain.experimental.gen... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
66ee35c90655-1 | arbitrary_types_allowed = True
# LLM-related methods
@staticmethod
def _parse_list(text: str) -> List[str]:
"""Parse a newline-separated string into a list of strings."""
lines = re.split(r"\n", text.strip())
return [re.sub(r"^\s*\d+\.\s*", "", line).strip() for line in lines]
de... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
66ee35c90655-2 | entity_action = self._get_entity_action(observation, entity_name)
q1 = f"What is the relationship between {self.name} and {entity_name}"
q2 = f"{entity_name} is {entity_action}"
return self.chain(prompt=prompt).run(q1=q1, queries=[q1, q2]).strip()
def _generate_reaction(self, observation: st... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
66ee35c90655-3 | return self.chain(prompt=prompt).run(**kwargs).strip()
def _clean_response(self, text: str) -> str:
return re.sub(f"^{self.name} ", "", text.strip()).strip()
[docs] def generate_reaction(self, observation: str) -> Tuple[bool, str]:
"""React to a given observation."""
call_to_action_templa... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
66ee35c90655-4 | """React to a given observation."""
call_to_action_template = (
"What would {agent_name} say? To end the conversation, write:"
' GOODBYE: "what to say". Otherwise to continue the conversation,'
' write: SAY: "what to say next"\n\n'
)
full_result = self._genera... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
66ee35c90655-5 | "How would you summarize {name}'s core characteristics given the"
+ " following statements:\n"
+ "{relevant_memories}"
+ "Do not embellish."
+ "\n\nSummary: "
)
# The agent seeks to think about their core characteristics.
return (
self.... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
66ee35c90655-6 | )
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
0b5f766121a5-0 | Source code for langchain.experimental.generative_agents.memory
import logging
import re
from typing import Any, Dict, List, Optional
from langchain import LLMChain
from langchain.base_language import BaseLanguageModel
from langchain.prompts import PromptTemplate
from langchain.retrievers import TimeWeightedVectorStore... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
0b5f766121a5-1 | relevant_memories_simple_key: str = "relevant_memories_simple"
most_recent_memories_key: str = "most_recent_memories"
def chain(self, prompt: PromptTemplate) -> LLMChain:
return LLMChain(llm=self.llm, prompt=prompt, verbose=self.verbose)
@staticmethod
def _parse_list(text: str) -> List[str]:
... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
0b5f766121a5-2 | + "What 5 high-level insights can you infer from the above statements?"
+ " (example format: insight (because of 1, 5, 3))"
)
related_memories = self.fetch_memories(topic)
related_statements = "\n".join(
[
f"{i+1}. {memory.page_content}"
fo... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
0b5f766121a5-3 | + "\nMemory: {memory_content}"
+ "\nRating: "
)
score = self.chain(prompt).run(memory_content=memory_content).strip()
if self.verbose:
logger.info(f"Importance score: {score}")
match = re.search(r"^\D*(\d+)", score)
if match:
return (float(scor... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
0b5f766121a5-4 | content = []
for mem in relevant_memories:
if mem.page_content in content_strs:
continue
content_strs.add(mem.page_content)
created_time = mem.metadata["created_at"].strftime("%B %d, %Y, %I:%M %p")
content.append(f"- {created_time}: {mem.page_conte... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
0b5f766121a5-5 | relevant_memories
),
self.relevant_memories_simple_key: self.format_memories_simple(
relevant_memories
),
}
most_recent_memories_token = inputs.get(self.most_recent_memories_token_key)
if most_recent_memories_token is not No... | https:///python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
0c3430986812-0 | 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 |
0c3430986812-1 | """
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 |
0c3430986812-2 | 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 |
0c3430986812-3 | 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 |
f7dd5ec0c2a2-0 | 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 |
f7dd5ec0c2a2-1 | 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 |
f7dd5ec0c2a2-2 | [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 |
8ec8956f5ffb-0 | Source code for langchain.retrievers.vespa_retriever
"""Wrapper for retrieving documents from Vespa."""
from __future__ import annotations
import json
from typing import TYPE_CHECKING, List
from langchain.schema import BaseRetriever, Document
if TYPE_CHECKING:
from vespa.application import Vespa
[docs]class VespaRe... | https:///python.langchain.com/en/latest/_modules/langchain/retrievers/vespa_retriever.html |
6d13cd243caf-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 |
6d13cd243caf-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 |
878fc7f4d544-0 | 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 |
878fc7f4d544-1 | 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 |
6742566a74ef-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 |
6742566a74ef-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 |
6742566a74ef-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 |
6f66422d69d3-0 | 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 |
bbc098aeae7e-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 |
bbc098aeae7e-1 | return list(compressed_docs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/_modules/langchain/retrievers/contextual_compression.html |
6bf6fa91f949-0 | 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 |
6bf6fa91f949-1 | """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 |
daf3147b9a66-0 | 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 |
daf3147b9a66-1 | 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 |
b588956a6bd3-0 | 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 |
b588956a6bd3-1 | 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 |
2e0d570bff24-0 | 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 |
b1e545e46634-0 | 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 |
b1e545e46634-1 | 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 |
9e9715e911b3-0 | 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 |
9e9715e911b3-1 | 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 |
7f1a0da7eb8e-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.base_language import Base... | https:///python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_filter.html |
7f1a0da7eb8e-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 |
3fbe180ab87f-0 | Source code for langchain.retrievers.document_compressors.cohere_rerank
from __future__ import annotations
from typing import TYPE_CHECKING, Dict, Sequence
from pydantic import root_validator
from langchain.retrievers.document_compressors.base import BaseDocumentCompressor
from langchain.schema import Document
from lan... | https:///python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/cohere_rerank.html |
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