id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
a962634a24e5-0 | Source code for langchain.embeddings.sagemaker_endpoint
"""Wrapper around Sagemaker InvokeEndpoint API."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.llms.sagemaker_endpoint import ContentHandlerBase
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
a962634a24e5-1 | If a specific credential profile should be used, you must pass
the name of the profile from the ~/.aws/credentials file that is to be used.
Make sure the credentials / roles used have the required policies to
access the Sagemaker endpoint.
See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_pol... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
a962634a24e5-2 | )
"""
client: Any #: :meta private:
endpoint_name: str = ""
"""The name of the endpoint from the deployed Sagemaker model.
Must be unique within an AWS Region."""
region_name: str = ""
"""The aws region where the Sagemaker model is deployed, eg. `us-west-2`."""
credentials_profile_name:... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
a962634a24e5-3 | output transform functions to handle formats between LLM
and the endpoint.
"""
"""
Example:
.. code-block:: python
from langchain.embeddings.sagemaker_endpoint import EmbeddingsContentHandler
class ContentHandler(EmbeddingsContentHandler):
content_type = "applica... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
a962634a24e5-4 | endpoint_kwargs: Optional[Dict] = None
"""Optional attributes passed to the invoke_endpoint
function. See `boto3`_. docs for more info.
.. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>
"""
class Config:
"""Configuration for this pydantic object."""
extr... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
a962634a24e5-5 | session = boto3.Session()
values["client"] = session.client(
"sagemaker-runtime", region_name=values["region_name"]
)
except Exception as e:
raise ValueError(
"Could not load credentials to authenticate with AWS client. ... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
a962634a24e5-6 | _model_kwargs = self.model_kwargs or {}
_endpoint_kwargs = self.endpoint_kwargs or {}
body = self.content_handler.transform_input(texts, _model_kwargs)
content_type = self.content_handler.content_type
accepts = self.content_handler.accepts
# send request
try:
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
a962634a24e5-7 | Args:
texts: The list of texts to embed.
chunk_size: The chunk size defines how many input texts will
be grouped together as request. If None, will use the
chunk size specified by the class.
Returns:
List of embeddings, one for each text.
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
a962634a24e5-8 | """
return self._embedding_func([text])[0] | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
c95d8a95089f-0 | Source code for langchain.embeddings.llamacpp
"""Wrapper around llama.cpp embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.embeddings.base import Embeddings
[docs]class LlamaCppEmbeddings(BaseModel, Embeddings):
"""Wrapper ... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
c95d8a95089f-1 | model_path: str
n_ctx: int = Field(512, alias="n_ctx")
"""Token context window."""
n_parts: int = Field(-1, alias="n_parts")
"""Number of parts to split the model into.
If -1, the number of parts is automatically determined."""
seed: int = Field(-1, alias="seed")
"""Seed. If -1, a random se... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
c95d8a95089f-2 | use_mlock: bool = Field(False, alias="use_mlock")
"""Force system to keep model in RAM."""
n_threads: Optional[int] = Field(None, alias="n_threads")
"""Number of threads to use. If None, the number
of threads is automatically determined."""
n_batch: Optional[int] = Field(8, alias="n_batch")
"""... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
c95d8a95089f-3 | """Validate that llama-cpp-python library is installed."""
model_path = values["model_path"]
model_param_names = [
"n_ctx",
"n_parts",
"seed",
"f16_kv",
"logits_all",
"vocab_only",
"use_mlock",
"n_threads",
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
c95d8a95089f-4 | raise ModuleNotFoundError(
"Could not import llama-cpp-python library. "
"Please install the llama-cpp-python library to "
"use this embedding model: pip install llama-cpp-python"
)
except Exception as e:
raise ValueError(
f... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
c95d8a95089f-5 | [docs] def embed_query(self, text: str) -> List[float]:
"""Embed a query using the Llama model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
embedding = self.client.embed(text)
return list(map(float, embedding)) | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
68d044c6d286-0 | Source code for langchain.embeddings.dashscope
"""Wrapper around DashScope embedding models."""
from __future__ import annotations
import logging
from typing import (
Any,
Callable,
Dict,
List,
Optional,
)
from pydantic import BaseModel, Extra, root_validator
from requests.exceptions import HTTPErro... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/dashscope.html |
68d044c6d286-1 | # 1 seconds, then up to 4 seconds, then 4 seconds afterwards
return retry(
reraise=True,
stop=stop_after_attempt(embeddings.max_retries),
wait=wait_exponential(multiplier, min=min_seconds, max=max_seconds),
retry=(retry_if_exception_type(HTTPError)),
before_sleep=before_sleep... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/dashscope.html |
68d044c6d286-2 | raise ValueError(
f"status_code: {resp.status_code} \n "
f"code: {resp.code} \n message: {resp.message}"
)
else:
raise HTTPError(
f"HTTP error occurred: status_code: {resp.status_code} \n "
f"code: {resp.code} \n message: {r... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/dashscope.html |
68d044c6d286-3 | embeddings = DashScopeEmbeddings(dashscope_api_key="my-api-key")
Example:
.. code-block:: python
import os
os.environ["DASHSCOPE_API_KEY"] = "your DashScope API KEY"
from langchain.embeddings.dashscope import DashScopeEmbeddings
embeddings = DashScopeEmbedding... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/dashscope.html |
68d044c6d286-4 | extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
import dashscope
"""Validate that api key and python package exists in environment."""
values["dashscope_api_key"] = get_from_dict_or_env(
values, "dashscope_api_key", "DASHSCOPE_API_K... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/dashscope.html |
68d044c6d286-5 | Args:
texts: The list of texts to embed.
chunk_size: The chunk size of embeddings. If None, will use the chunk size
specified by the class.
Returns:
List of embeddings, one for each text.
"""
embeddings = embed_with_retry(
self, inp... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/dashscope.html |
68d044c6d286-6 | )[0]["embedding"]
return embedding | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/dashscope.html |
f05d60bb5daf-0 | Source code for langchain.memory.motorhead_memory
from typing import Any, Dict, List, Optional
import requests
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema import get_buffer_string
MANAGED_URL = "https://api.getmetal.io/v1/motorhead"
# LOCAL_URL = "http://localhost:8080"
[docs]class Mot... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/motorhead_memory.html |
f05d60bb5daf-1 | }
if is_managed and not (self.api_key and self.client_id):
raise ValueError(
"""
You must provide an API key or a client ID to use the managed
version of Motorhead. Visit https://getmetal.io for more information.
"""
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/motorhead_memory.html |
f05d60bb5daf-2 | context = res_data.get("context", "NONE")
for message in reversed(messages):
if message["role"] == "AI":
self.chat_memory.add_ai_message(message["content"])
else:
self.chat_memory.add_user_message(message["content"])
if context and context != "NONE... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/motorhead_memory.html |
f05d60bb5daf-3 | input_str, output_str = self._get_input_output(inputs, outputs)
requests.post(
f"{self.url}/sessions/{self.session_id}/memory",
timeout=self.timeout,
json={
"messages": [
{"role": "Human", "content": f"{input_str}"},
{"r... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/motorhead_memory.html |
c6e353beb67b-0 | Source code for langchain.memory.entity
import logging
from abc import ABC, abstractmethod
from itertools import islice
from typing import Any, Dict, Iterable, List, Optional
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langch... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
c6e353beb67b-1 | pass
@abstractmethod
def set(self, key: str, value: Optional[str]) -> None:
"""Set entity value in store."""
pass
@abstractmethod
def delete(self, key: str) -> None:
"""Delete entity value from store."""
pass
@abstractmethod
def exists(self, key: str) -> bool:
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
c6e353beb67b-2 | return self.store.get(key, default)
[docs] def set(self, key: str, value: Optional[str]) -> None:
self.store[key] = value
[docs] def delete(self, key: str) -> None:
del self.store[key]
[docs] def exists(self, key: str) -> bool:
return key in self.store
[docs] def clear(self) -> None:... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
c6e353beb67b-3 | recall_ttl: Optional[int] = 60 * 60 * 24 * 3
def __init__(
self,
session_id: str = "default",
url: str = "redis://localhost:6379/0",
key_prefix: str = "memory_store",
ttl: Optional[int] = 60 * 60 * 24,
recall_ttl: Optional[int] = 60 * 60 * 24 * 3,
*args: Any,
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
c6e353beb67b-4 | except redis.exceptions.ConnectionError as error:
logger.error(error)
self.session_id = session_id
self.key_prefix = key_prefix
self.ttl = ttl
self.recall_ttl = recall_ttl or ttl
@property
def full_key_prefix(self) -> str:
return f"{self.key_prefix}:{self.sess... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
c6e353beb67b-5 | if not value:
return self.delete(key)
self.redis_client.set(f"{self.full_key_prefix}:{key}", value, ex=self.ttl)
logger.debug(
f"REDIS MEM set '{self.full_key_prefix}:{key}': '{value}' EX {self.ttl}"
)
[docs] def delete(self, key: str) -> None:
self.redis_clien... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
c6e353beb67b-6 | yield batch
for keybatch in batched(
self.redis_client.scan_iter(f"{self.full_key_prefix}:*"), 500
):
self.redis_client.delete(*keybatch)
[docs]class SQLiteEntityStore(BaseEntityStore):
"""SQLite-backed Entity store"""
session_id: str = "default"
table_name: str = "me... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
c6e353beb67b-7 | )
super().__init__(*args, **kwargs)
self.conn = sqlite3.connect(db_file)
self.session_id = session_id
self.table_name = table_name
self._create_table_if_not_exists()
@property
def full_table_name(self) -> str:
return f"{self.table_name}_{self.session_id}"
def ... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
c6e353beb67b-8 | WHERE key = ?
"""
cursor = self.conn.execute(query, (key,))
result = cursor.fetchone()
if result is not None:
value = result[0]
return value
return default
[docs] def set(self, key: str, value: Optional[str]) -> None:
if not value:
r... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
c6e353beb67b-9 | [docs] def exists(self, key: str) -> bool:
query = f"""
SELECT 1
FROM {self.full_table_name}
WHERE key = ?
LIMIT 1
"""
cursor = self.conn.execute(query, (key,))
result = cursor.fetchone()
return result is not None
[docs] def c... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
c6e353beb67b-10 | SQLite, or other entity store.
"""
human_prefix: str = "Human"
ai_prefix: str = "AI"
llm: BaseLanguageModel
entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT
entity_summarization_prompt: BasePromptTemplate = ENTITY_SUMMARIZATION_PROMPT
# Cache of recently detected entit... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
c6e353beb67b-11 | return self.chat_memory.messages
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return ["entities", self.chat_history_key]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
c6e353beb67b-12 | if self.input_key is None:
prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
else:
prompt_input_key = self.input_key
# Extract an arbitrary window of the last message pairs from
# the chat history, where the hyperparameter k is the
# number of... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
c6e353beb67b-13 | if output.strip() == "NONE":
entities = []
else:
# Make a list of the extracted entities:
entities = [w.strip() for w in output.split(",")]
# Make a dictionary of entities with summary if exists:
entity_summaries = {}
for entity in entities:
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
c6e353beb67b-14 | buffer = buffer_string
return {
self.chat_history_key: buffer,
"entities": entity_summaries,
}
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""
Save context from this conversation history to the entity store.
G... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
c6e353beb67b-15 | buffer_string = get_buffer_string(
self.buffer[-self.k * 2 :],
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
input_data = inputs[prompt_input_key]
# Create an LLMChain for predicting entity summarization from the context
chain = LLMChain(... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
c6e353beb67b-16 | [docs] def clear(self) -> None:
"""Clear memory contents."""
self.chat_memory.clear()
self.entity_cache.clear()
self.entity_store.clear() | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
f2ab7e5b01eb-0 | Source code for langchain.memory.buffer
from typing import Any, Dict, List, Optional
from pydantic import root_validator
from langchain.memory.chat_memory import BaseChatMemory, BaseMemory
from langchain.memory.utils import get_prompt_input_key
from langchain.schema import get_buffer_string
[docs]class ConversationBuff... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/buffer.html |
f2ab7e5b01eb-1 | )
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return [self.memory_key]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return history buffer."""
retur... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/buffer.html |
f2ab7e5b01eb-2 | @root_validator()
def validate_chains(cls, values: Dict) -> Dict:
"""Validate that return messages is not True."""
if values.get("return_messages", False):
raise ValueError(
"return_messages must be False for ConversationStringBufferMemory"
)
return va... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/buffer.html |
f2ab7e5b01eb-3 | if self.input_key is None:
prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
else:
prompt_input_key = self.input_key
if self.output_key is None:
if len(outputs) != 1:
raise ValueError(f"One output key expected, got {outputs.keys()}... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/buffer.html |
970ced87c4e8-0 | Source code for langchain.memory.vectorstore
"""Class for a VectorStore-backed memory object."""
from typing import Any, Dict, List, Optional, Union
from pydantic import Field
from langchain.memory.chat_memory import BaseMemory
from langchain.memory.utils import get_prompt_input_key
from langchain.schema import Documen... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/vectorstore.html |
970ced87c4e8-1 | return_docs: bool = False
"""Whether or not to return the result of querying the database directly."""
@property
def memory_variables(self) -> List[str]:
"""The list of keys emitted from the load_memory_variables method."""
return [self.memory_key]
def _get_prompt_input_key(self, inputs:... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/vectorstore.html |
970ced87c4e8-2 | result: Union[List[Document], str]
if not self.return_docs:
result = "\n".join([doc.page_content for doc in docs])
else:
result = docs
return {self.memory_key: result}
def _form_documents(
self, inputs: Dict[str, Any], outputs: Dict[str, str]
) -> List[Doc... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/vectorstore.html |
970ced87c4e8-3 | [docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
documents = self._form_documents(inputs, outputs)
self.retriever.add_documents(documents)
[docs] def clear(self) -> None:
"""Nothing to clear... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/vectorstore.html |
747ec64fba7b-0 | Source code for langchain.memory.buffer_window
from typing import Any, Dict, List
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema import BaseMessage, get_buffer_string
[docs]class ConversationBufferWindowMemory(BaseChatMemory):
"""Buffer for storing conversation memory."""
human_pr... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/buffer_window.html |
747ec64fba7b-1 | return [self.memory_key]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
"""Return history buffer."""
buffer: Any = self.buffer[-self.k * 2 :] if self.k > 0 else []
if not self.return_messages:
buffer = get_buffer_string(
buffer,
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/buffer_window.html |
7be18352fe16-0 | Source code for langchain.memory.summary
from __future__ import annotations
from typing import Any, Dict, List, Type
from pydantic import BaseModel, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.memory.chat_memory import BaseChatMemory
from... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/summary.html |
7be18352fe16-1 | def predict_new_summary(
self, messages: List[BaseMessage], existing_summary: str
) -> str:
new_lines = get_buffer_string(
messages,
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
chain = LLMChain(llm=self.llm, prompt=self.prompt)
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/summary.html |
7be18352fe16-2 | **kwargs: Any,
) -> ConversationSummaryMemory:
obj = cls(llm=llm, chat_memory=chat_memory, **kwargs)
for i in range(0, len(obj.chat_memory.messages), summarize_step):
obj.buffer = obj.predict_new_summary(
obj.chat_memory.messages[i : i + summarize_step], obj.buffer
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/summary.html |
7be18352fe16-3 | else:
buffer = self.buffer
return {self.memory_key: buffer}
@root_validator()
def validate_prompt_input_variables(cls, values: Dict) -> Dict:
"""Validate that prompt input variables are consistent."""
prompt_variables = values["prompt"].input_variables
expected_keys =... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/summary.html |
7be18352fe16-4 | )
[docs] def clear(self) -> None:
"""Clear memory contents."""
super().clear()
self.buffer = "" | https://api.python.langchain.com/en/latest/_modules/langchain/memory/summary.html |
e7a7c1799aea-0 | Source code for langchain.memory.combined
import warnings
from typing import Any, Dict, List, Set
from pydantic import validator
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema import BaseMemory
[docs]class CombinedMemory(BaseMemory):
"""Class for combining multiple memories' data toge... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/combined.html |
e7a7c1799aea-1 | )
all_variables |= set(val.memory_variables)
return value
@validator("memories")
def check_input_key(cls, value: List[BaseMemory]) -> List[BaseMemory]:
"""Check that if memories are of type BaseChatMemory that input keys exist."""
for val in value:
if isinstance(v... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/combined.html |
e7a7c1799aea-2 | memory_variables.extend(memory.memory_variables)
return memory_variables
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
"""Load all vars from sub-memories."""
memory_data: Dict[str, Any] = {}
# Collect vars from all sub-memories
for memory in... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/combined.html |
e7a7c1799aea-3 | """Clear context from this session for every memory."""
for memory in self.memories:
memory.clear() | https://api.python.langchain.com/en/latest/_modules/langchain/memory/combined.html |
41a692094270-0 | Source code for langchain.memory.readonly
from typing import Any, Dict, List
from langchain.schema import BaseMemory
[docs]class ReadOnlySharedMemory(BaseMemory):
"""A memory wrapper that is read-only and cannot be changed."""
memory: BaseMemory
@property
def memory_variables(self) -> List[str]:
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/readonly.html |
b27c9c00671e-0 | Source code for langchain.memory.simple
from typing import Any, Dict, List
from langchain.schema import BaseMemory
[docs]class SimpleMemory(BaseMemory):
"""Simple memory for storing context or other bits of information that shouldn't
ever change between prompts.
"""
memories: Dict[str, Any] = dict()
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/simple.html |
b27c9c00671e-1 | pass | https://api.python.langchain.com/en/latest/_modules/langchain/memory/simple.html |
63252e705c9e-0 | Source code for langchain.memory.token_buffer
from typing import Any, Dict, List
from langchain.base_language import BaseLanguageModel
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema import BaseMessage, get_buffer_string
[docs]class ConversationTokenBufferMemory(BaseChatMemory):
"""Buf... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/token_buffer.html |
63252e705c9e-1 | return [self.memory_key]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return history buffer."""
buffer: Any = self.buffer
if self.return_messages:
final_buffer: Any = buffer
else:
final_buffer = get_buffer_string(
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/token_buffer.html |
63252e705c9e-2 | if curr_buffer_length > self.max_token_limit:
pruned_memory = []
while curr_buffer_length > self.max_token_limit:
pruned_memory.append(buffer.pop(0))
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer) | https://api.python.langchain.com/en/latest/_modules/langchain/memory/token_buffer.html |
9e340c3c9858-0 | Source code for langchain.memory.summary_buffer
from typing import Any, Dict, List
from pydantic import root_validator
from langchain.memory.chat_memory import BaseChatMemory
from langchain.memory.summary import SummarizerMixin
from langchain.schema import BaseMessage, get_buffer_string
[docs]class ConversationSummaryB... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/summary_buffer.html |
9e340c3c9858-1 | return [self.memory_key]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return history buffer."""
buffer = self.buffer
if self.moving_summary_buffer != "":
first_messages: List[BaseMessage] = [
self.summary_message_cls(content=... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/summary_buffer.html |
9e340c3c9858-2 | expected_keys = {"summary", "new_lines"}
if expected_keys != set(prompt_variables):
raise ValueError(
"Got unexpected prompt input variables. The prompt expects "
f"{prompt_variables}, but it should have {expected_keys}."
)
return values
[docs] ... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/summary_buffer.html |
9e340c3c9858-3 | pruned_memory.append(buffer.pop(0))
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
self.moving_summary_buffer = self.predict_new_summary(
pruned_memory, self.moving_summary_buffer
)
[docs] def clear(self) -> None:
"""Clear memory con... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/summary_buffer.html |
2302e4359a9e-0 | Source code for langchain.memory.kg
from typing import Any, Dict, List, Type, Union
from pydantic import Field
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.graphs import NetworkxEntityGraph
from langchain.graphs.networkx_graph import KnowledgeTriple, get... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
2302e4359a9e-1 | Integrates with external knowledge graph to store and retrieve
information about knowledge triples in the conversation.
"""
k: int = 2
human_prefix: str = "Human"
ai_prefix: str = "AI"
kg: NetworkxEntityGraph = Field(default_factory=NetworkxEntityGraph)
knowledge_extraction_prompt: BasePromp... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
2302e4359a9e-2 | summary_strings = []
for entity in entities:
knowledge = self.kg.get_entity_knowledge(entity)
if knowledge:
summary = f"On {entity}: {'. '.join(knowledge)}."
summary_strings.append(summary)
context: Union[str, List]
if not summary_strings:
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
2302e4359a9e-3 | """Get the input key for the prompt."""
if self.input_key is None:
return get_prompt_input_key(inputs, self.memory_variables)
return self.input_key
def _get_prompt_output_key(self, outputs: Dict[str, Any]) -> str:
"""Get the output key for the prompt."""
if self.output_ke... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
2302e4359a9e-4 | ai_prefix=self.ai_prefix,
)
output = chain.predict(
history=buffer_string,
input=input_string,
)
return get_entities(output)
def _get_current_entities(self, inputs: Dict[str, Any]) -> List[str]:
"""Get the current entities in the conversation."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
2302e4359a9e-5 | history=buffer_string,
input=input_string,
verbose=True,
)
knowledge = parse_triples(output)
return knowledge
def _get_and_update_kg(self, inputs: Dict[str, Any]) -> None:
"""Get and update knowledge graph from the conversation history."""
prompt_input... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
2302e4359a9e-6 | super().clear()
self.kg.clear() | https://api.python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
1b7aadb01908-0 | Source code for langchain.memory.chat_message_histories.zep
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Dict, List, Optional
from langchain.schema import (
AIMessage,
BaseChatMessageHistory,
BaseMessage,
HumanMessage,
)
if TYPE_CHECKING:
from zep_python import... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/zep.html |
1b7aadb01908-1 | memory = ConversationBufferMemory(
memory_key="chat_history", chat_memory=zep_chat_history
)
Zep provides long-term conversation storage for LLM apps. The server stores,
summarizes, embeds, indexes, and enriches conversational AI chat
histories, and exposes them via simple, low-latency A... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/zep.html |
1b7aadb01908-2 | ) -> None:
try:
from zep_python import ZepClient
except ImportError:
raise ValueError(
"Could not import zep-python package. "
"Please install it with `pip install zep-python`."
)
self.zep_client = ZepClient(base_url=url)
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/zep.html |
1b7aadb01908-3 | if zep_memory.messages:
msg: Message
for msg in zep_memory.messages:
if msg.role == "ai":
messages.append(AIMessage(content=msg.content))
else:
messages.append(HumanMessage(content=msg.content))
return messages
@... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/zep.html |
1b7aadb01908-4 | return zep_memory.summary.content
def _get_memory(self) -> Optional[Memory]:
"""Retrieve memory from Zep"""
from zep_python import NotFoundError
try:
zep_memory: Memory = self.zep_client.get_memory(self.session_id)
except NotFoundError:
logger.warning(
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/zep.html |
1b7aadb01908-5 | zep_memory = Memory(messages=[zep_message])
self.zep_client.add_memory(self.session_id, zep_memory)
[docs] def search(
self, query: str, metadata: Optional[Dict] = None, limit: Optional[int] = None
) -> List[MemorySearchResult]:
"""Search Zep memory for messages matching the query"""
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/zep.html |
1b7aadb01908-6 | """
try:
self.zep_client.delete_memory(self.session_id)
except NotFoundError:
logger.warning(
f"Session {self.session_id} not found in Zep. Skipping delete."
) | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/zep.html |
785dc7e0a3ee-0 | Source code for langchain.memory.chat_message_histories.cosmos_db
"""Azure CosmosDB Memory History."""
from __future__ import annotations
import logging
from types import TracebackType
from typing import TYPE_CHECKING, Any, List, Optional, Type
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
785dc7e0a3ee-1 | ttl: Optional[int] = None,
cosmos_client_kwargs: Optional[dict] = None,
):
"""
Initializes a new instance of the CosmosDBChatMessageHistory class.
Make sure to call prepare_cosmos or use the context manager to make
sure your database is ready.
Either a credential or a... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
785dc7e0a3ee-2 | :param ttl: The time to live (in seconds) to use for documents in the container.
:param cosmos_client_kwargs: Additional kwargs to pass to the CosmosClient.
"""
self.cosmos_endpoint = cosmos_endpoint
self.cosmos_database = cosmos_database
self.cosmos_container = cosmos_container
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
785dc7e0a3ee-3 | ) from exc
if self.credential:
self._client = CosmosClient(
url=self.cosmos_endpoint,
credential=self.credential,
**cosmos_client_kwargs or {},
)
elif self.conn_string:
self._client = CosmosClient.from_connection_string(... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
785dc7e0a3ee-4 | PartitionKey,
)
except ImportError as exc:
raise ImportError(
"You must install the azure-cosmos package to use the CosmosDBChatMessageHistory." # noqa: E501
) from exc
database = self._client.create_database_if_not_exists(self.cosmos_database)
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
785dc7e0a3ee-5 | exc_val: Optional[BaseException],
traceback: Optional[TracebackType],
) -> None:
"""Context manager exit"""
self.upsert_messages()
self._client.__exit__(exc_type, exc_val, traceback)
[docs] def load_messages(self) -> None:
"""Retrieve the messages from Cosmos"""
if... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
785dc7e0a3ee-6 | item = self._container.read_item(
item=self.session_id, partition_key=self.user_id
)
except CosmosHttpResponseError:
logger.info("no session found")
return
if "messages" in item and len(item["messages"]) > 0:
self.messages = messages_from_d... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
785dc7e0a3ee-7 | "user_id": self.user_id,
"messages": messages_to_dict(self.messages),
}
)
[docs] def clear(self) -> None:
"""Clear session memory from this memory and cosmos."""
self.messages = []
if self._container:
self._container.delete_item(
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
e9b9d1f8684e-0 | Source code for langchain.memory.chat_message_histories.in_memory
from typing import List
from pydantic import BaseModel
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
)
[docs]class ChatMessageHistory(BaseChatMessageHistory, BaseModel):
messages: List[BaseMessage] = []
[docs] def add... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/in_memory.html |
bd137fe8f7c0-0 | Source code for langchain.memory.chat_message_histories.cassandra
import json
import logging
from typing import List
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
_message_to_dict,
messages_from_dict,
)
logger = logging.getLogger(__name__)
DEFAULT_KEYSPACE_NAME = "chat_history"
DEF... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cassandra.html |
bd137fe8f7c0-1 | username: username to connect to Cassandra cluster
password: password to connect to Cassandra cluster
keyspace_name: name of the keyspace to use
table_name: name of the table to use
"""
def __init__(
self,
contact_points: List[str],
session_id: str,
port: ... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cassandra.html |
bd137fe8f7c0-2 | AuthenticationFailed,
OperationTimedOut,
UnresolvableContactPoints,
)
from cassandra.cluster import Cluster, PlainTextAuthProvider
except ImportError:
raise ValueError(
"Could not import cassandra-driver python package. "
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cassandra.html |
bd137fe8f7c0-3 | self._prepare_cassandra()
def _prepare_cassandra(self) -> None:
"""Create the keyspace and table if they don't exist yet"""
from cassandra import OperationTimedOut, Unavailable
try:
self.session.execute(
f"""CREATE KEYSPACE IF NOT EXISTS
{self.key... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cassandra.html |
bd137fe8f7c0-4 | f"""CREATE TABLE IF NOT EXISTS
{self.table_name} (id UUID, session_id varchar,
history text, PRIMARY KEY ((session_id), id) );"""
)
except (OperationTimedOut, Unavailable) as error:
logger.error(
f"Unable to create cassandra \
... | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cassandra.html |
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