id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 53 121 |
|---|---|---|
f50ec90d5116-4 | suffix_to_use = suffix
if include_df_in_prompt:
dfs_head = "\n\n".join([d.head().to_markdown() for d in dfs])
suffix_to_use = suffix_to_use.format(
dfs_head=dfs_head,
)
elif include_df_in_prompt:
dfs_head = "\n\n".join([d.head().to_markdown() for d... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/pandas/base.html |
f50ec90d5116-5 | if include_df_in_prompt is not None and suffix is not None:
raise ValueError("If suffix is specified, include_df_in_prompt should not be.")
if isinstance(df, list):
for item in df:
if not isinstance(item, pd.DataFrame):
raise ValueError(f"Expected pandas object, got {type... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/pandas/base.html |
f50ec90d5116-6 | agent: BaseSingleActionAgent
if agent_type == AgentType.ZERO_SHOT_REACT_DESCRIPTION:
prompt, tools = _get_prompt_and_tools(
df,
prefix=prefix,
suffix=suffix,
input_variables=input_variables,
include_df_in_prompt=include_df_in_prompt,
)
... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/pandas/base.html |
db60b2750580-0 | Source code for langchain.agents.agent_toolkits.json.toolkit
"""Toolkit for interacting with a JSON spec."""
from __future__ import annotations
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.json.tool import JsonGetValueTool... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/json/toolkit.html |
e53502d50397-0 | Source code for langchain.agents.agent_toolkits.json.base
"""Json agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/json/base.html |
e53502d50397-1 | return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/json/base.html |
77749ed9e6f5-0 | Source code for langchain.agents.self_ask_with_search.base
"""Chain that does self ask with search."""
from typing import Any, Sequence, Union
from pydantic import Field
from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langchain.agents.agent_types import AgentType
from langchain.agents.se... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/self_ask_with_search/base.html |
77749ed9e6f5-1 | raise ValueError(f"Exactly one tool must be specified, but got {tools}")
tool_names = {tool.name for tool in tools}
if tool_names != {"Intermediate Answer"}:
raise ValueError(
f"Tool name should be Intermediate Answer, got {tool_names}"
)
@property
def obs... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/self_ask_with_search/base.html |
5e79e95f21e2-0 | 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://api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
5e79e95f21e2-1 | 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://api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
5e79e95f21e2-2 | 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://api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
5e79e95f21e2-3 | """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://api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
5e79e95f21e2-4 | 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 Controller."""
... | https://api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
57796e7ab5f9-0 | 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://api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
57796e7ab5f9-1 | @classmethod
def from_llm_and_tools(
cls,
ai_name: str,
ai_role: str,
memory: VectorStoreRetriever,
tools: List[BaseTool],
llm: BaseChatModel,
human_in_the_loop: bool = False,
output_parser: Optional[BaseAutoGPTOutputParser] = None,
chat_histor... | https://api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
57796e7ab5f9-2 | user_input=user_input,
)
# Print Assistant thoughts
print(assistant_reply)
self.chat_history_memory.add_message(HumanMessage(content=user_input))
self.chat_history_memory.add_message(AIMessage(content=assistant_reply))
# Get command name and argume... | https://api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
57796e7ab5f9-3 | return "EXITING"
memory_to_add += feedback
self.memory.add_documents([Document(page_content=memory_to_add)])
self.chat_history_memory.add_message(SystemMessage(content=result)) | https://api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
cd927b7e5792-0 | Source code for langchain.experimental.generative_agents.memory
import logging
import re
from datetime import datetime
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 ... | https://api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/memory.html |
cd927b7e5792-1 | # output keys
relevant_memories_key: str = "relevant_memories"
relevant_memories_simple_key: str = "relevant_memories_simple"
most_recent_memories_key: str = "most_recent_memories"
now_key: str = "now"
reflecting: bool = False
def chain(self, prompt: PromptTemplate) -> LLMChain:
return L... | https://api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/memory.html |
cd927b7e5792-2 | self, topic: str, now: Optional[datetime] = None
) -> List[str]:
"""Generate 'insights' on a topic of reflection, based on pertinent memories."""
prompt = PromptTemplate.from_template(
"Statements relevant to: '{topic}'\n"
"---\n"
"{related_statements}\n"
... | https://api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/memory.html |
cd927b7e5792-3 | insights = self._get_insights_on_topic(topic, now=now)
for insight in insights:
self.add_memory(insight, now=now)
new_insights.extend(insights)
return new_insights
def _score_memory_importance(self, memory_content: str) -> float:
"""Score the absolute importan... | https://api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/memory.html |
cd927b7e5792-4 | + " acceptance), rate the likely poignancy of the"
+ " following piece of memory. Always answer with only a list of numbers."
+ " If just given one memory still respond in a list."
+ " Memories are separated by semi colans (;)"
+ "\Memories: {memory_content}"
... | https://api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/memory.html |
cd927b7e5792-5 | and not self.reflecting
):
self.reflecting = True
self.pause_to_reflect(now=now)
# Hack to clear the importance from reflection
self.aggregate_importance = 0.0
self.reflecting = False
return result
[docs] def add_memory(
self, memory... | https://api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/memory.html |
cd927b7e5792-6 | else:
return self.memory_retriever.get_relevant_documents(observation)
def format_memories_detail(self, relevant_memories: List[Document]) -> str:
content = []
for mem in relevant_memories:
content.append(self._format_memory_detail(mem, prefix="- "))
return "\n".join(... | https://api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/memory.html |
cd927b7e5792-7 | now = inputs.get(self.now_key)
if queries is not None:
relevant_memories = [
mem for query in queries for mem in self.fetch_memories(query, now=now)
]
return {
self.relevant_memories_key: self.format_memories_detail(
relevan... | https://api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/memory.html |
d8e7afffb7d0-0 | 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://api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/generative_agent.html |
d8e7afffb7d0-1 | 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://api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/generative_agent.html |
d8e7afffb7d0-2 | 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, observ... | https://api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/generative_agent.html |
d8e7afffb7d0-3 | )
consumed_tokens = self.llm.get_num_tokens(
prompt.format(most_recent_memories="", **kwargs)
)
kwargs[self.memory.most_recent_memories_token_key] = consumed_tokens
return self.chain(prompt=prompt).run(**kwargs).strip()
def _clean_response(self, text: str) -> str:
... | https://api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/generative_agent.html |
d8e7afffb7d0-4 | if "SAY:" in result:
said_value = self._clean_response(result.split("SAY:")[-1])
return True, f"{self.name} said {said_value}"
else:
return False, result
[docs] def generate_dialogue_response(
self, observation: str, now: Optional[datetime] = None
) -> Tuple[bo... | https://api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/generative_agent.html |
d8e7afffb7d0-5 | )
return True, f"{self.name} said {response_text}"
else:
return False, result
######################################################
# Agent stateful' summary methods. #
# Each dialog or response prompt includes a header #
# summarizing the agent's sel... | https://api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/generative_agent.html |
d8e7afffb7d0-6 | + f"\nInnate traits: {self.traits}"
+ f"\n{self.summary}"
)
[docs] def get_full_header(
self, force_refresh: bool = False, now: Optional[datetime] = None
) -> str:
"""Return a full header of the agent's status, summary, and current time."""
now = datetime.now() if now ... | https://api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/generative_agent.html |
c0953299026d-0 | Source code for langchain.vectorstores.typesense
"""Wrapper around Typesense vector search"""
from __future__ import annotations
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Union
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
fro... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/typesense.html |
c0953299026d-1 | typesense_client: Client,
embedding: Embeddings,
*,
typesense_collection_name: Optional[str] = None,
text_key: str = "text",
):
"""Initialize with Typesense client."""
try:
from typesense import Client
except ImportError:
raise ValueErr... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/typesense.html |
c0953299026d-2 | for _id, vec, text, metadata in zip(_ids, embedded_texts, texts, _metadatas)
]
def _create_collection(self, num_dim: int) -> None:
fields = [
{"name": "vec", "type": "float[]", "num_dim": num_dim},
{"name": f"{self._text_key}", "type": "string"},
{"name": ".*", "t... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/typesense.html |
c0953299026d-3 | return [doc["id"] for doc in docs]
[docs] def similarity_search_with_score(
self,
query: str,
k: int = 10,
filter: Optional[str] = "",
) -> List[Tuple[Document, float]]:
"""Return typesense documents most similar to query, along with scores.
Args:
query... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/typesense.html |
c0953299026d-4 | ) -> List[Document]:
"""Return typesense documents most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 10.
Minimum 10 results would be returned.
filter: typesense filter_by expression ... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/typesense.html |
c0953299026d-5 | "Please install it with `pip install typesense`."
)
node = {
"host": host,
"port": str(port),
"protocol": protocol,
}
typesense_api_key = typesense_api_key or get_from_env(
"typesense_api_key", "TYPESENSE_API_KEY"
)
clie... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/typesense.html |
ee538ed776f5-0 | Source code for langchain.vectorstores.supabase
from __future__ import annotations
import uuid
from itertools import repeat
from typing import (
TYPE_CHECKING,
Any,
Iterable,
List,
Optional,
Tuple,
Type,
Union,
)
import numpy as np
from langchain.docstore.document import Document
from la... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/supabase.html |
ee538ed776f5-1 | embedding: Embeddings,
table_name: str,
query_name: Union[str, None] = None,
) -> None:
"""Initialize with supabase client."""
try:
import supabase # noqa: F401
except ImportError:
raise ValueError(
"Could not import supabase python pa... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/supabase.html |
ee538ed776f5-2 | """Return VectorStore initialized from texts and embeddings."""
if not client:
raise ValueError("Supabase client is required.")
if not table_name:
raise ValueError("Supabase document table_name is required.")
embeddings = embedding.embed_documents(texts)
ids = [st... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/supabase.html |
ee538ed776f5-3 | ) -> List[Tuple[Document, float]]:
vectors = self._embedding.embed_documents([query])
return self.similarity_search_by_vector_with_relevance_scores(vectors[0], k)
[docs] def similarity_search_by_vector_with_relevance_scores(
self, query: List[float], k: int
) -> List[Tuple[Document, float... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/supabase.html |
ee538ed776f5-4 | ),
)
for search in res.data
if search.get("content")
]
return match_result
@staticmethod
def _texts_to_documents(
texts: Iterable[str],
metadatas: Optional[Iterable[dict[Any, Any]]] = None,
) -> List[Document]:
"""Return list of Doc... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/supabase.html |
ee538ed776f5-5 | if len(result.data) == 0:
raise Exception("Error inserting: No rows added")
# VectorStore.add_vectors returns ids as strings
ids = [str(i.get("id")) for i in result.data if i.get("id")]
id_list.extend(ids)
return id_list
[docs] def max_marginal_relevance_se... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/supabase.html |
ee538ed776f5-6 | matched_embeddings,
k=k,
lambda_mult=lambda_mult,
)
filtered_documents = [matched_documents[i] for i in mmr_selected]
return filtered_documents
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/supabase.html |
ee538ed776f5-7 | SELECT
id,
content,
metadata,
embedding,
1 -(docstore.embedding <=> query_embedding) AS similarity
FROM
docstore
ORDER BY
docstore.embedding <=> query_embedding
LIMIT match... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/supabase.html |
499e023589c9-0 | Source code for langchain.vectorstores.cassandra
"""Wrapper around Cassandra vector-store capabilities, based on cassIO."""
from __future__ import annotations
import hashlib
import typing
from typing import Any, Iterable, List, Optional, Tuple, Type, TypeVar
import numpy as np
if typing.TYPE_CHECKING:
from cassandr... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/cassandra.html |
499e023589c9-1 | )
return self._embedding_dimension
def __init__(
self,
embedding: Embeddings,
session: Session,
keyspace: str,
table_name: str,
ttl_seconds: int | None = CASSANDRA_VECTORSTORE_DEFAULT_TTL_SECONDS,
) -> None:
try:
from cassio.vector impo... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/cassandra.html |
499e023589c9-2 | ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts (Iterable[str]): Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/cassandra.html |
499e023589c9-3 | """Return docs most similar to embedding vector.
No support for `filter` query (on metadata) along with vector search.
Args:
embedding (str): Embedding to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
Returns:
List of (Do... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/cassandra.html |
499e023589c9-4 | """Return docs most similar to embedding vector.
No support for `filter` query (on metadata) along with vector search.
Args:
embedding (str): Embedding to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
Returns:
List of (Do... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/cassandra.html |
499e023589c9-5 | embedding_vector,
k,
)
# Even though this is a `_`-method,
# it is apparently used by VectorSearch parent class
# in an exposed method (`similarity_search_with_relevance_scores`).
# So we implement it (hmm).
def _similarity_search_with_relevance_scores(
self,
quer... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/cassandra.html |
499e023589c9-6 | metric="cos",
metric_threshold=None,
)
# let the mmr utility pick the *indices* in the above array
mmrChosenIndices = maximal_marginal_relevance(
np.array(embedding, dtype=np.float32),
[pfHit["embedding_vector"] for pfHit in prefetchHits],
k=k,
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/cassandra.html |
499e023589c9-7 | return self.max_marginal_relevance_search_by_vector(
embedding_vector,
k,
fetch_k,
lambda_mult=lambda_mult,
)
[docs] @classmethod
def from_texts(
cls: Type[CVST],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[L... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/cassandra.html |
499e023589c9-8 | return cls.from_texts(
texts=texts,
metadatas=metadatas,
embedding=embedding,
session=session,
keyspace=keyspace,
table_name=table_name,
) | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/cassandra.html |
f93d527147eb-0 | Source code for langchain.vectorstores.alibabacloud_opensearch
import json
import logging
import numbers
from hashlib import sha1
from typing import Any, Dict, Iterable, List, Optional, Tuple
from langchain.embeddings.base import Embeddings
from langchain.schema import Document
from langchain.vectorstores.base import V... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
f93d527147eb-1 | instance_id: str
username: str
password: str
datasource_name: str
embedding_index_name: str
field_name_mapping: Dict[str, str] = {
"id": "id",
"document": "document",
"embedding": "embedding",
"metadata_field_x": "metadata_field_x,operator",
}
def __init__(
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
f93d527147eb-2 | def __init__(
self,
embedding: Embeddings,
config: AlibabaCloudOpenSearchSettings,
**kwargs: Any,
) -> None:
try:
from alibabacloud_ha3engine import client, models
from alibabacloud_tea_util import models as util_models
except ImportError:
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
f93d527147eb-3 | self.config.datasource_name, field_name_map["id"], push_request
)
json_response = json.loads(push_response.body)
if json_response["status"] == "OK":
return [
push_doc["fields"][field_name_map["id"]]
for p... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
f93d527147eb-4 | )
if metadata is not None:
for md_key, md_value in metadata.items():
add_doc_fields.__setitem__(
field_name_map[md_key].split(",")[0], md_value
)
add_doc.__setitem__("fields", add_doc_fields)
add_doc.__se... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
f93d527147eb-5 | embedding=embedding, search_filter=search_filter, k=k
)
)
[docs] def inner_embedding_query(
self,
embedding: List[float],
search_filter: Optional[Dict[str, Any]] = None,
k: int = 4,
) -> Dict[str, Any]:
def generate_embedding_query() -> str:
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
f93d527147eb-6 | md_filter_operator = expr[1].strip()
if isinstance(md_value, numbers.Number):
return f"{md_filter_key} {md_filter_operator} {md_value}"
return f'{md_filter_key}{md_filter_operator}"{md_value}"'
def search_data(single_query_str: str) -> Dict[str, Any]:
search_q... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
f93d527147eb-7 | self, json_result: Dict[str, Any]
) -> List[Tuple[Document, float]]:
items = json_result["result"]["items"]
query_result_list: List[Tuple[Document, float]] = []
for item in items:
fields = item["fields"]
query_result_list.append(
(
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
f93d527147eb-8 | return cls.from_texts(
texts=texts,
embedding=embedding,
metadatas=metadatas,
config=config,
**kwargs,
) | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
5bfef6dfaf0e-0 | Source code for langchain.vectorstores.starrocks
"""Wrapper around open source StarRocks VectorSearch capability."""
from __future__ import annotations
import json
import logging
from hashlib import sha1
from threading import Thread
from typing import Any, Dict, Iterable, List, Optional, Tuple
from pydantic import Base... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/starrocks.html |
5bfef6dfaf0e-1 | for idx, datum in enumerate(value):
k = columns[idx][0]
r[k] = datum
result.append(r)
debug_output(result)
cursor.close()
return result
class StarRocksSettings(BaseSettings):
"""StarRocks Client Configuration
Attribute:
StarRocks_host (str) : An URL to connect... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/starrocks.html |
5bfef6dfaf0e-2 | database: str = "default"
table: str = "langchain"
def __getitem__(self, item: str) -> Any:
return getattr(self, item)
class Config:
env_file = ".env"
env_prefix = "starrocks_"
env_file_encoding = "utf-8"
[docs]class StarRocks(VectorStore):
"""Wrapper around StarRocks vec... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/starrocks.html |
5bfef6dfaf0e-3 | self.pgbar = lambda x, **kwargs: x
super().__init__()
if config is not None:
self.config = config
else:
self.config = StarRocksSettings()
assert self.config
assert self.config.host and self.config.port
assert self.config.column_map and self.config.... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/starrocks.html |
5bfef6dfaf0e-4 | def _build_insert_sql(self, transac: Iterable, column_names: Iterable[str]) -> str:
ks = ",".join(column_names)
embed_tuple_index = tuple(column_names).index(
self.config.column_map["embedding"]
)
_data = []
for n in transac:
n = ",".join(
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/starrocks.html |
5bfef6dfaf0e-5 | metadata: Optional column data to be inserted
Returns:
List of ids from adding the texts into the VectorStore.
"""
# Embed and create the documents
ids = ids or [sha1(t.encode("utf-8")).hexdigest() for t in texts]
colmap_ = self.config.column_map
transac = []
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/starrocks.html |
5bfef6dfaf0e-6 | return []
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[Dict[Any, Any]]] = None,
config: Optional[StarRocksSettings] = None,
text_ids: Optional[Iterable[str]] = None,
batch_size: int = 32,
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/starrocks.html |
5bfef6dfaf0e-7 | _repr += f"\033[1musername: {self.config.username}\033[0m\n\nTable Schema:\n"
width = 25
fields = 3
_repr += "-" * (width * fields + 1) + "\n"
columns = ["name", "type", "key"]
_repr += f"|\033[94m{columns[0]:24s}\033[0m|\033[96m{columns[1]:24s}"
_repr += f"\033[0m|\033[9... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/starrocks.html |
5bfef6dfaf0e-8 | q_str = f"""
SELECT {self.config.column_map['document']},
{self.config.column_map['metadata']},
cosine_similarity_norm(array<float>[{q_emb_str}],
{self.config.column_map['embedding']}) as dist
FROM {self.config.database}.{self.config.table}
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/starrocks.html |
5bfef6dfaf0e-9 | """Perform a similarity search with StarRocks by vectors
Args:
query (str): query string
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional): where condition string.
Defaults to No... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/starrocks.html |
5bfef6dfaf0e-10 | where_str (Optional[str], optional): where condition string.
Defaults to None.
NOTE: Please do not let end-user to fill this and always be aware
of SQL injection. When dealing with metadatas, remember to
use `{self.metadata... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/starrocks.html |
f36b61fa669d-0 | Source code for langchain.vectorstores.awadb
"""Wrapper around AwaDB for embedding vectors"""
from __future__ import annotations
import logging
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import ... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/awadb.html |
f36b61fa669d-1 | self.table2embeddings: dict[str, Embeddings] = {}
if embedding is not None:
self.table2embeddings[table_name] = embedding
self.using_table_name = table_name
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
is_duplica... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/awadb.html |
f36b61fa669d-2 | [docs] def similarity_search(
self,
query: str,
k: int = DEFAULT_TOPN,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query."""
if self.awadb_client is None:
raise ValueError("AwaDB client is None!!!")
embedding = None
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/awadb.html |
f36b61fa669d-3 | retrieval_docs = self.similarity_search_by_vector(embedding, k, scores)
L2_Norm = 0.0
for score in scores:
L2_Norm = L2_Norm + score * score
L2_Norm = pow(L2_Norm, 0.5)
doc_no = 0
for doc in retrieval_docs:
doc_tuple = (doc, 1 - (scores[doc_no] / L2_Norm))... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/awadb.html |
f36b61fa669d-4 | L2_Norm = L2_Norm + score * score
L2_Norm = pow(L2_Norm, 0.5)
doc_no = 0
for doc in retrieval_docs:
doc_tuple = (doc, 1 - scores[doc_no] / L2_Norm)
results.append(doc_tuple)
doc_no = doc_no + 1
return results
[docs] def similarity_search_by_vector(
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/awadb.html |
f36b61fa669d-5 | content = item_detail[item_key]
elif (
item_key == "Field@1" or item_key == "text_embedding"
): # embedding field for the document
continue
elif item_key == "score": # L2 distance
if scores is not None:
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/awadb.html |
f36b61fa669d-6 | ) -> str:
"""Get the current table."""
return self.using_table_name
[docs] @classmethod
def from_texts(
cls: Type[AwaDB],
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
table_name: str = _DEFAULT_TABLE_NAME... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/awadb.html |
f36b61fa669d-7 | table_name: str = _DEFAULT_TABLE_NAME,
log_and_data_dir: Optional[str] = None,
client: Optional[awadb.Client] = None,
**kwargs: Any,
) -> AwaDB:
"""Create an AwaDB vectorstore from a list of documents.
If a log_and_data_dir specified, the table will be persisted there.
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/awadb.html |
9c7f89b11a7a-0 | Source code for langchain.vectorstores.weaviate
"""Wrapper around weaviate vector database."""
from __future__ import annotations
import datetime
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type
from uuid import uuid4
import numpy as np
from langchain.docstore.document import Document
from ... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
9c7f89b11a7a-1 | if weaviate_api_key is not None
else None
)
client = weaviate.Client(weaviate_url, auth_client_secret=auth)
return client
def _default_score_normalizer(val: float) -> float:
return 1 - 1 / (1 + np.exp(val))
def _json_serializable(value: Any) -> Any:
if isinstance(value, datetime.datetime):
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
9c7f89b11a7a-2 | )
if not isinstance(client, weaviate.Client):
raise ValueError(
f"client should be an instance of weaviate.Client, got {type(client)}"
)
self._client = client
self._index_name = index_name
self._embedding = embedding
self._text_key = text_k... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
9c7f89b11a7a-3 | if self._embedding is not None:
vector = self._embedding.embed_documents([text])[0]
else:
vector = None
batch.add_data_object(
data_object=data_properties,
class_name=self._index_name,
uui... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
9c7f89b11a7a-4 | if kwargs.get("search_distance"):
content["certainty"] = kwargs.get("search_distance")
query_obj = self._client.query.get(self._index_name, self._query_attrs)
if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter"))
if kwargs.get("additi... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
9c7f89b11a7a-5 | docs.append(Document(page_content=text, metadata=res))
return docs
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
9c7f89b11a7a-6 | **kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
9c7f89b11a7a-7 | return docs
[docs] def similarity_search_with_score(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""
Return list of documents most similar to the query
text and cosine distance in float for each.
Lower score represents more similarity.
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
9c7f89b11a7a-8 | return docs_and_scores
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores, normalized on a scale from 0 to 1.
0 is dissimilar, 1 is most similar.
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
9c7f89b11a7a-9 | weaviate = Weaviate.from_texts(
texts,
embeddings,
weaviate_url="http://localhost:8080"
)
"""
client = _create_weaviate_client(**kwargs)
from weaviate.util import get_valid_uuid
index_name = kwargs.get("index_nam... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
9c7f89b11a7a-10 | "class_name": index_name,
}
if embeddings is not None:
params["vector"] = embeddings[i]
batch.add_data_object(**params)
batch.flush()
relevance_score_fn = kwargs.get("relevance_score_fn")
by_text: bool = kwargs.get("by_text"... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
510b6adefef9-0 | Source code for langchain.vectorstores.rocksetdb
"""Wrapper around Rockset vector database."""
from __future__ import annotations
import logging
from enum import Enum
from typing import Any, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/rocksetdb.html |
510b6adefef9-1 | client: Any,
embeddings: Embeddings,
collection_name: str,
text_key: str,
embedding_key: str,
):
"""Initialize with Rockset client.
Args:
client: Rockset client object
collection: Rockset collection to insert docs / query
embeddings... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/rocksetdb.html |
510b6adefef9-2 | """Run more texts through the embeddings and add to the vectorstore
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of ids to associate with the texts.
batch_si... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/rocksetdb.html |
510b6adefef9-3 | ) -> Rockset:
"""Create Rockset wrapper with existing texts.
This is intended as a quicker way to get started.
"""
# Sanitize imputs
assert client is not None, "Rockset Client cannot be None"
assert collection_name, "Collection name cannot be empty"
assert text_ke... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/rocksetdb.html |
510b6adefef9-4 | k (int, optional): Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional): Metadata filters supplied as a
SQL `where` condition string. Defaults to None.
eg. "price<=70.0 AND brand='Nintendo'"
NOTE: Please do not let end-user to fill this ... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/rocksetdb.html |
510b6adefef9-5 | """Accepts a query_embedding (vector), and returns documents with
similar embeddings."""
docs_and_scores = self.similarity_search_by_vector_with_relevance_scores(
embedding, k, distance_func, where_str, **kwargs
)
return [doc for doc, _ in docs_and_scores]
[docs] def simil... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/rocksetdb.html |
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