id stringlengths 14 16 | text stringlengths 31 3.14k | source stringlengths 58 124 |
|---|---|---|
d6d5b25c9fa0-4 | text = "This is a test text"
doc_result = embeddings.embed_documents([text])
query_result = embeddings.embed_query(text)
"""
def _embed(self, text: str) -> List[float]:
try:
from aleph_alpha_client import (
Prompt,
SemanticEmbeddingRequ... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
d6d5b25c9fa0-5 | Returns:
List of embeddings, one for each text.
"""
document_embeddings = []
for text in texts:
document_embeddings.append(self._embed(text))
return document_embeddings
[docs] def embed_query(self, text: str) -> List[float]:
"""Call out to Aleph Alpha's... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
c03db3029d58-0 | Source code for langchain.embeddings.tensorflow_hub
"""Wrapper around TensorflowHub embedding models."""
from typing import Any, List
from pydantic import BaseModel, Extra
from langchain.embeddings.base import Embeddings
DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
[docs]clas... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/tensorflow_hub.html |
c03db3029d58-1 | ) from e
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a TensorflowHub embedding model.
Args:
texts: The list of texts to embed.
... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/tensorflow_hub.html |
6cf2a6524308-0 | 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
... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
6cf2a6524308-1 | endpoint_name = (
"my-endpoint-name"
)
region_name = (
"us-west-2"
)
credentials_profile_name = (
"default"
)
se = SagemakerEndpointEmbeddings(
endpoint_name=endpoint_name,
... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
6cf2a6524308-2 | class ContentHandler(EmbeddingsContentHandler):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompts: List[str], model_kwargs: Dict) -> bytes:
input_str = json.dumps({prompts: prompts, **model_kwargs})
... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
6cf2a6524308-3 | if values["credentials_profile_name"] is not None:
session = boto3.Session(
profile_name=values["credentials_profile_name"]
)
else:
# use default credentials
session = boto3.Session()
... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
6cf2a6524308-4 | Body=body,
ContentType=content_type,
Accept=accepts,
**_endpoint_kwargs,
)
except Exception as e:
raise ValueError(f"Error raised by inference endpoint: {e}")
return self.content_handler.transform_output(response["Body"])
[docs] ... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
6cf2a6524308-5 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
ad2b2ab40837-0 | Source code for langchain.embeddings.huggingface_hub
"""Wrapper around HuggingFace Hub embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_REPO_ID... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
ad2b2ab40837-1 | """Task to call the model with."""
model_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model."""
huggingfacehub_api_token: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_env... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
ad2b2ab40837-2 | )
values["client"] = client
except ImportError:
raise ValueError(
"Could not import huggingface_hub python package. "
"Please install it with `pip install huggingface_hub`."
)
return values
[docs] def embed_documents(self, texts: Lis... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
48398b2c2f24-0 | Source code for langchain.embeddings.self_hosted
"""Running custom embedding models on self-hosted remote hardware."""
from typing import Any, Callable, List
from pydantic import Extra
from langchain.embeddings.base import Embeddings
from langchain.llms import SelfHostedPipeline
def _embed_documents(pipeline: Any, *arg... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
48398b2c2f24-1 | model_id = "facebook/bart-large"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
return pipeline("feature-extraction", model=model, tokenizer=tokenizer)
embeddings = SelfHostedEmbeddings(
... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
48398b2c2f24-2 | class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a HuggingFace transformer model.
Args:
texts: The list of texts to embed.s
Retu... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
1f36ae388e50-0 | Source code for langchain.embeddings.cohere
"""Wrapper around Cohere embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class CohereEmbeddings(Base... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html |
1f36ae388e50-1 | """Validate that api key and python package exists in environment."""
cohere_api_key = get_from_dict_or_env(
values, "cohere_api_key", "COHERE_API_KEY"
)
try:
import cohere
values["client"] = cohere.Client(cohere_api_key)
except ImportError:
... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html |
1f36ae388e50-2 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html |
c4a24790c843-0 | Source code for langchain.embeddings.openai
"""Wrapper around OpenAI embedding models."""
from __future__ import annotations
import logging
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Set,
Tuple,
Union,
)
import numpy as np
from pydantic import BaseModel, Extra... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
c4a24790c843-1 | | retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
"""Use tenacity to retry the e... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
c4a24790c843-2 | API_VERSION.
The OPENAI_API_TYPE must be set to 'azure' and the others correspond to
the properties of your endpoint.
In addition, the deployment name must be passed as the model parameter.
Example:
.. code-block:: python
import os
os.environ["OPENAI_API_TYPE"] = "azure"
... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
c4a24790c843-3 | chunk_size: int = 1000
"""Maximum number of texts to embed in each batch"""
max_retries: int = 6
"""Maximum number of retries to make when generating."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cl... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
c4a24790c843-4 | ) -> List[List[float]]:
embeddings: List[List[float]] = [[] for i in range(len(texts))]
try:
import tiktoken
tokens = []
indices = []
encoding = tiktoken.model.encoding_for_model(self.model)
for i, text in enumerate(texts):
# re... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
c4a24790c843-5 | lens[indices[i]].append(len(batched_embeddings[i]))
for i in range(len(texts)):
_result = results[i]
if len(_result) == 0:
average = embed_with_retry(self, input="", engine=self.deployment)[
"data"
][0]["embeddin... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
c4a24790c843-6 | ) -> List[List[float]]:
"""Call out to OpenAI's embedding endpoint for embedding search docs.
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:
L... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
c0afe36ad171-0 | Source code for langchain.embeddings.self_hosted_hugging_face
"""Wrapper around HuggingFace embedding models for self-hosted remote hardware."""
import importlib
import logging
from typing import Any, Callable, List, Optional
from langchain.embeddings.self_hosted import SelfHostedEmbeddings
DEFAULT_MODEL_NAME = "senten... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
c0afe36ad171-1 | client = INSTRUCTOR(model_id)
if importlib.util.find_spec("torch") is not None:
import torch
cuda_device_count = torch.cuda.device_count()
if device < -1 or (device >= cuda_device_count):
raise ValueError(
f"Got device=={device}, "
f"device is requ... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
c0afe36ad171-2 | import runhouse as rh
model_name = "sentence-transformers/all-mpnet-base-v2"
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
hf = SelfHostedHuggingFaceEmbeddings(model_name=model_name, hardware=gpu)
"""
client: Any #: :meta private:
model_id: str = DEFAULT_MODEL... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
c0afe36ad171-3 | load_fn_kwargs["instruct"] = load_fn_kwargs.get("instruct", False)
load_fn_kwargs["device"] = load_fn_kwargs.get("device", 0)
super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
[docs]class SelfHostedHuggingFaceInstructEmbeddings(SelfHostedHuggingFaceEmbeddings):
"""Runs InstructorEmbedding em... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
c0afe36ad171-4 | embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
"""Instruction to use for embedding documents."""
query_instruction: str = DEFAULT_QUERY_INSTRUCTION
"""Instruction to use for embedding query."""
model_reqs: List[str] = ["./", "InstructorEmbedding", "torch"]
"""Requirements to install on hardware ... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
c0afe36ad171-5 | """
instruction_pairs = []
for text in texts:
instruction_pairs.append([self.embed_instruction, text])
embeddings = self.client(self.pipeline_ref, instruction_pairs)
return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
b5ed624fe7b7-0 | Source code for langchain.embeddings.fake
from typing import List
import numpy as np
from pydantic import BaseModel
from langchain.embeddings.base import Embeddings
[docs]class FakeEmbeddings(Embeddings, BaseModel):
size: int
def _get_embedding(self) -> List[float]:
return list(np.random.normal(size=sel... | /content/https://python.langchain.com/en/latest/_modules/langchain/embeddings/fake.html |
9d6ad98bd423-0 | Source code for langchain.chat_models.azure_openai
"""Azure OpenAI chat wrapper."""
from __future__ import annotations
import logging
from typing import Any, Dict
from pydantic import root_validator
from langchain.chat_models.openai import ChatOpenAI
from langchain.utils import get_from_dict_or_env
logger = logging.get... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
9d6ad98bd423-1 | in, even if not explicitly saved on this class.
"""
deployment_name: str = ""
openai_api_type: str = "azure"
openai_api_base: str = ""
openai_api_version: str = ""
openai_api_key: str = ""
openai_organization: str = ""
@root_validator()
def validate_environment(cls, values: Dict) -> ... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
9d6ad98bd423-2 | if openai_organization:
openai.organization = openai_organization
except ImportError:
raise ValueError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
try:
values["client"] = ... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
de537338d997-0 | Source code for langchain.chat_models.openai
"""OpenAI chat wrapper."""
from __future__ import annotations
import logging
import sys
from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple
from pydantic import Extra, Field, root_validator
from tenacity import (
before_sleep_log,
retry,
retry_... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
de537338d997-1 | | retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
async def acompletion_with_retry(llm:... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
de537338d997-2 | return SystemMessage(content=_dict["content"])
else:
return ChatMessage(content=_dict["content"], role=role)
def _convert_message_to_dict(message: BaseMessage) -> dict:
if isinstance(message, ChatMessage):
message_dict = {"role": message.role, "content": message.content}
elif isinstance(mess... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
de537338d997-3 | .. code-block:: python
from langchain.chat_models import ChatOpenAI
openai = ChatOpenAI(model_name="gpt-3.5-turbo")
"""
client: Any #: :meta private:
model_name: str = "gpt-3.5-turbo"
"""Model name to use."""
temperature: float = 0.7
"""What sampling temperature to use."... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
de537338d997-4 | """Build extra kwargs from additional params that were passed in."""
all_required_field_names = {field.alias for field in cls.__fields__.values()}
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name not in all_required_field_names:
if ... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
de537338d997-5 | "`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`."
)
if values["n"] < 1:
raise ValueError("n must be at least 1.")
if values["n"] > 1... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
de537338d997-6 | retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceU... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
de537338d997-7 | else:
overall_token_usage[k] = v
return {"token_usage": overall_token_usage, "model_name": self.model_name}
def _generate(
self, messages: List[BaseMessage], stop: Optional[List[str]] = None
) -> ChatResult:
message_dicts, params = self._create_message_dicts(messages,... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
de537338d997-8 | ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
params: Dict[str, Any] = {**{"model": self.model_name}, **self._default_params}
if stop is not None:
if "stop" in params:
raise ValueError("`stop` found in both the input and default params.")
params["stop"] = stop... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
de537338d997-9 | self, messages=message_dicts, **params
):
role = stream_resp["choices"][0]["delta"].get("role", role)
token = stream_resp["choices"][0]["delta"].get("content", "")
inner_completion += token
if self.callback_manager.is_async:
... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
de537338d997-10 | return super().get_num_tokens(text)
try:
import tiktoken
except ImportError:
raise ValueError(
"Could not import tiktoken python package. "
"This is needed in order to calculate get_num_tokens. "
"Please install it with `pip install... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
de537338d997-11 | # Returning num tokens assuming gpt-3.5-turbo-0301.
model = "gpt-3.5-turbo-0301"
elif model == "gpt-4":
# gpt-4 may change over time.
# Returning num tokens assuming gpt-4-0314.
model = "gpt-4-0314"
# Returns the number of tokens used by a list of messages... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
de537338d997-12 | for message in messages_dict:
num_tokens += tokens_per_message
for key, value in message.items():
num_tokens += len(encoding.encode(value))
if key == "name":
num_tokens += tokens_per_name
# every reply is primed with <im_start>assistant... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
d2afde6159ca-0 | Source code for langchain.chat_models.promptlayer_openai
"""PromptLayer wrapper."""
import datetime
from typing import List, Optional
from langchain.chat_models import ChatOpenAI
from langchain.schema import BaseMessage, ChatResult
[docs]class PromptLayerChatOpenAI(ChatOpenAI):
"""Wrapper around OpenAI Chat large l... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
d2afde6159ca-1 | ) -> ChatResult:
"""Call ChatOpenAI generate and then call PromptLayer API to log the request."""
from promptlayer.utils import get_api_key, promptlayer_api_request
request_start_time = datetime.datetime.now().timestamp()
generated_responses = super()._generate(messages, stop)
re... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
d2afde6159ca-2 | ) -> ChatResult:
"""Call ChatOpenAI agenerate and then call PromptLayer to log."""
from promptlayer.utils import get_api_key, promptlayer_api_request_async
request_start_time = datetime.datetime.now().timestamp()
generated_responses = await super()._agenerate(messages, stop)
requ... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
d580cf680916-0 | Source code for langchain.chat_models.anthropic
from typing import Any, Dict, List, Optional
from pydantic import Extra
from langchain.chat_models.base import BaseChatModel
from langchain.llms.anthropic import _AnthropicCommon
from langchain.schema import (
AIMessage,
BaseMessage,
ChatGeneration,
ChatMe... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
d580cf680916-1 | elif isinstance(message, HumanMessage):
message_text = f"{self.HUMAN_PROMPT} {message.content}"
elif isinstance(message, AIMessage):
message_text = f"{self.AI_PROMPT} {message.content}"
elif isinstance(message, SystemMessage):
message_text = f"{self.HUMAN_PROMPT} <adm... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
d580cf680916-2 | if not isinstance(messages[-1], AIMessage):
messages.append(AIMessage(content=""))
text = self._convert_messages_to_text(messages)
return (
text.rstrip()
) # trim off the trailing ' ' that might come from the "Assistant: "
def _generate(
self, messages: List[... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
d580cf680916-3 | prompt = self._convert_messages_to_prompt(messages)
params: Dict[str, Any] = {"prompt": prompt, **self._default_params}
if stop:
params["stop_sequences"] = stop
if self.streaming:
completion = ""
stream_resp = await self.client.acompletion_stream(**params)
... | /content/https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
311adb2e5e85-0 | .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 ... | /content/https://python.langchain.com/en/latest/tracing/hosted_installation.html |
311adb2e5e85-1 | An example of adding all relevant environment variables is below:
import os
os.environ["LANGCHAIN_HANDLER"] = "langchain"
os.environ["LANGCHAIN_ENDPOINT"] = "https://langchain-api-gateway-57eoxz8z.uc.gateway.dev"
os.environ["LANGCHAIN_API_KEY"] = "my_api_key" # Don't commit this to your repo! Better to set it in your ... | /content/https://python.langchain.com/en/latest/tracing/hosted_installation.html |
7c50766b3932-0 | .ipynb
.pdf
Tracing Walkthrough
Tracing Walkthrough#
import os
os.environ["LANGCHAIN_HANDLER"] = "langchain"
## Uncomment this if using hosted setup.
# os.environ["LANGCHAIN_ENDPOINT"] = "https://langchain-api-gateway-57eoxz8z.uc.gateway.dev"
## Uncomment this if you want traces to be recorded to "my_session" instead ... | /content/https://python.langchain.com/en/latest/tracing/agent_with_tracing.html |
7c50766b3932-1 | 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, ChatOpenAI(temperature=0), agent=AgentType.ZER... | /content/https://python.langchain.com/en/latest/tracing/agent_with_tracing.html |
7c50766b3932-2 | "action_input": "2^0.123243"
}
```
Observation: Answer: 1.0891804557407723
Thought:The final answer is 1.0891804557407723.
Final Answer: 1.0891804557407723
> Finished chain.
'1.0891804557407723'
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/tracing/agent_with_tracing.html |
2a0b29f878d5-0 | .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... | /content/https://python.langchain.com/en/latest/tracing/local_installation.html |
2a0b29f878d5-1 | import os
os.environ["LANGCHAIN_HANDLER"] = "langchain"
Contents
Installation
Environment Setup
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/tracing/local_installation.html |
5e7a66fdc41f-0 | .md
.pdf
Question Answering over Docs
Contents
Document Question Answering
Adding in sources
Additional Related Resources
End-to-end examples
Question Answering over Docs#
Conceptual Guide
Question answering in this context refers to question answering over your document data.
For question answering over other types ... | /content/https://python.langchain.com/en/latest/use_cases/question_answering.html |
5e7a66fdc41f-1 | Document Question Answering#
Question answering involves fetching multiple documents, and then asking a question of them.
The LLM response will contain the answer to your question, based on the content of the documents.
The recommended way to get started using a question answering chain is:
from langchain.chains.questi... | /content/https://python.langchain.com/en/latest/use_cases/question_answering.html |
5e7a66fdc41f-2 | QA With Sources Notebook: A notebook walking through how to accomplish this task.
VectorDB QA With Sources Notebook: A notebook walking through how to do question answering with sources over a vector database. This can often be useful for when you have a LOT of documents, and you don’t want to pass them all to the LLM,... | /content/https://python.langchain.com/en/latest/use_cases/question_answering.html |
34a7d9557dd1-0 | .md
.pdf
Agent Simulations
Contents
Simulations with Two Agents
Simulations with Multiple Agents
Agent Simulations#
Agent simulations involve interacting one of more agents with eachother.
Agent simulations generally involve two main components:
Long Term Memory
Simulation Environment
Specific implementations of agen... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations.html |
81a582f00587-0 | .md
.pdf
Summarization
Summarization#
Conceptual Guide
Summarization involves creating a smaller summary of multiple longer documents.
This can be useful for distilling long documents into the core pieces of information.
The recommended way to get started using a summarization chain is:
from langchain.chains.summarize ... | /content/https://python.langchain.com/en/latest/use_cases/summarization.html |
014d1ea2b63c-0 | .md
.pdf
Chatbots
Chatbots#
Conceptual Guide
Since language models are good at producing text, that makes them ideal for creating chatbots.
Aside from the base prompts/LLMs, an important concept to know for Chatbots is memory.
Most chat based applications rely on remembering what happened in previous interactions, whic... | /content/https://python.langchain.com/en/latest/use_cases/chatbots.html |
9bdb258ea963-0 | .md
.pdf
Extraction
Extraction#
Conceptual Guide
Most APIs and databases still deal with structured information.
Therefore, in order to better work with those, it can be useful to extract structured information from text.
Examples of this include:
Extracting a structured row to insert into a database from a sentence
Ex... | /content/https://python.langchain.com/en/latest/use_cases/extraction.html |
260551e9d95d-0 | .md
.pdf
Code Understanding
Contents
Conversational Retriever Chain
Code Understanding#
Overview
LangChain is a useful tool designed to parse GitHub code repositories. By leveraging VectorStores, Conversational RetrieverChain, and GPT-4, it can answer questions in the context of an entire GitHub repository or generat... | /content/https://python.langchain.com/en/latest/use_cases/code.html |
260551e9d95d-1 | Build the Conversational Chain: Customize the retriever settings and define any user-defined filters as needed.
Ask questions: Define a list of questions to ask about the codebase, and then use the ConversationalRetrievalChain to generate context-aware answers. The LLM (GPT-4) generates comprehensive, context-aware ans... | /content/https://python.langchain.com/en/latest/use_cases/code.html |
148822ccfd30-0 | .md
.pdf
Querying Tabular Data
Contents
Document Loading
Querying
Chains
Agents
Querying Tabular Data#
Conceptual Guide
Lots of data and information is stored in tabular data, whether it be csvs, excel sheets, or SQL tables.
This page covers all resources available in LangChain for working with data in this format.
D... | /content/https://python.langchain.com/en/latest/use_cases/tabular.html |
b9e313a5b0d3-0 | .md
.pdf
Autonomous Agents
Contents
Baby AGI (Original Repo)
AutoGPT (Original Repo)
MetaPrompt (Original Repo)
Autonomous Agents#
Autonomous Agents are agents that designed to be more long running.
You give them one or multiple long term goals, and they independently execute towards those goals.
The applications com... | /content/https://python.langchain.com/en/latest/use_cases/autonomous_agents.html |
a9d6d8b9521c-0 | .md
.pdf
Personal Assistants (Agents)
Personal Assistants (Agents)#
Conceptual Guide
We use “personal assistant” here in a very broad sense.
Personal assistants have a few characteristics:
They can interact with the outside world
They have knowledge of your data
They remember your interactions
Really all of the functio... | /content/https://python.langchain.com/en/latest/use_cases/personal_assistants.html |
f5e996ebf9f3-0 | .rst
.pdf
Evaluation
Contents
The Problem
The Solution
The Examples
Other Examples
Evaluation#
Note
Conceptual Guide
This section of documentation covers how we approach and think about evaluation in LangChain.
Both evaluation of internal chains/agents, but also how we would recommend people building on top of LangCh... | /content/https://python.langchain.com/en/latest/use_cases/evaluation.html |
f5e996ebf9f3-1 | We have started LangChainDatasets a Community space on Hugging Face.
We intend this to be a collection of open source datasets for evaluating common chains and agents.
We have contributed five datasets of our own to start, but we highly intend this to be a community effort.
In order to contribute a dataset, you simply ... | /content/https://python.langchain.com/en/latest/use_cases/evaluation.html |
f5e996ebf9f3-2 | The existing examples we have are:
Question Answering (State of Union): A notebook showing evaluation of a question-answering task over a State-of-the-Union address.
Question Answering (Paul Graham Essay): A notebook showing evaluation of a question-answering task over a Paul Graham essay.
SQL Question Answering (Chino... | /content/https://python.langchain.com/en/latest/use_cases/evaluation.html |
3eccf1eda7fc-0 | .md
.pdf
Interacting with APIs
Contents
Chains
Agents
Interacting with APIs#
Conceptual Guide
Lots of data and information is stored behind APIs.
This page covers all resources available in LangChain for working with APIs.
Chains#
If you are just getting started, and you have relatively simple apis, you should get st... | /content/https://python.langchain.com/en/latest/use_cases/apis.html |
458f626cf1db-0 | .ipynb
.pdf
Generative Agents in LangChain
Contents
Generative Agent Memory Components
Memory Lifecycle
Create a Generative Character
Pre-Interview with Character
Step through the day’s observations.
Interview after the day
Adding Multiple Characters
Pre-conversation interviews
Dialogue between Generative Agents
Let’... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
458f626cf1db-1 | Memory Formation
Generative Agents have extended memories, stored in a single stream:
Observations - from dialogues or interactions with the virtual world, about self or others
Reflections - resurfaced and summarized core memories
Memory Recall
Memories are retrieved using a weighted sum of salience, recency, and impor... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
458f626cf1db-2 | # This will differ depending on a few things:
# - the distance / similarity metric used by the VectorStore
# - the scale of your embeddings (OpenAI's are unit norm. Many others are not!)
# This function converts the euclidean norm of normalized embeddings
# (0 is most similar, sqrt(2) most dissimilar)
... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
458f626cf1db-3 | status="looking for a job", # When connected to a virtual world, we can have the characters update their status
memory_retriever=create_new_memory_retriever(),
llm=LLM,
memory=tommies_memory
)
# The current "Summary" of a character can't be made because the agent h... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
458f626cf1db-4 | Pre-Interview with Character#
Before sending our character on their way, let’s ask them a few questions.
def interview_agent(agent: GenerativeAgent, message: str) -> str:
"""Help the notebook user interact with the agent."""
new_message = f"{USER_NAME} says {message}"
return agent.generate_dialogue_response... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
458f626cf1db-5 | "Tommie gets out of bed and heads to the kitchen to make himself some coffee.",
"Tommie realizes he forgot to buy coffee filters and starts rummaging through his moving boxes to find some.",
"Tommie finally finds the filters and makes himself a cup of coffee.",
"The coffee tastes bitter, and Tommie regrets ... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
458f626cf1db-6 | "Tommie has fun playing frisbee but gets hit in the face with the frisbee and hurts his nose.",
"Tommie goes back to his apartment to rest for a bit.",
"A raccoon tore open the trash bag outside his apartment, and the garbage is all over the floor.",
"Tommie starts to feel frustrated with his job search.",
... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
458f626cf1db-7 | Tommie finally finds the filters and makes himself a cup of coffee. Tommie takes a sip of the coffee and smiles, feeling a bit more awake and energized.
The coffee tastes bitter, and Tommie regrets not buying a better brand. Tommie grimaces and sets down the coffee, disappointed in the taste.
Tommie checks his email an... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
458f626cf1db-8 | Tommie overhears a conversation at the next table about a job opening. Tommie said "Excuse me, I couldn't help but overhear your conversation about the job opening. Do you have any more information about it?"
Tommie asks the diners about the job opening and gets some information about the company. Tommie said "Thank yo... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
458f626cf1db-9 | Tommie has fun playing frisbee but gets hit in the face with the frisbee and hurts his nose. Tommie winces and touches his nose, feeling a bit of pain.
Tommie goes back to his apartment to rest for a bit. Tommie takes a deep breath and sinks into his couch, feeling grateful for a moment of relaxation.
A raccoon tore op... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
458f626cf1db-10 | interview_agent(tommie, "Tell me about your childhood dog!")
'Tommie said "I actually didn\'t have a childhood dog, but I\'ve always loved animals. Do you have any pets?"'
Adding Multiple Characters#
Let’s add a second character to have a conversation with Tommie. Feel free to configure different traits.
eves_memory = ... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
458f626cf1db-11 | ]
for observation in eve_observations:
eve.memory.add_memory(observation)
print(eve.get_summary())
Name: Eve (age: 34)
Innate traits: curious, helpful
Eve is a helpful and active person who enjoys playing tennis, maintaining a healthy diet, and staying aware of her surroundings. She is a responsible employee who is... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
458f626cf1db-12 | Dialogue between Generative Agents#
Generative agents are much more complex when they interact with a virtual environment or with each other. Below, we run a simple conversation between Tommie and Eve.
def run_conversation(agents: List[GenerativeAgent], initial_observation: str) -> None:
"""Runs a conversation betw... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
458f626cf1db-13 | Tommie said "Thank you, Eve. That's really helpful advice. Did you have any specific ways of networking that worked well for you?"
Eve said "Sure, Tommie. I found that attending industry events and connecting with professionals on LinkedIn were both great ways to network. Do you have any specific questions about those ... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
458f626cf1db-14 | Innate traits: anxious, likes design, talkative
Tommie is a hopeful and proactive individual who is searching for a job. He becomes discouraged when he doesn't receive any offers or positive responses, but he tries to stay productive and calm by updating his resume, going for walks, and talking to friends for support. ... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
458f626cf1db-15 | interview_agent(eve, "What do you wish you would have said to Tommie?")
'Eve said "Well, I think I covered most of the topics Tommie was interested in, but if I had to add one thing, it would be to make sure to follow up with any connections you make during your job search. It\'s important to maintain those relationshi... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
f3aaf80350d4-0 | .ipynb
.pdf
CAMEL Role-Playing Autonomous Cooperative Agents
Contents
Import LangChain related modules
Define a CAMEL agent helper class
Setup OpenAI API key and roles and task for role-playing
Create a task specify agent for brainstorming and get the specified task
Create inception prompts for AI assistant and AI us... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
f3aaf80350d4-1 | Overview:
The rapid advancement of conversational and chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming. This paper explores the potential of building scalable tec... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
f3aaf80350d4-2 | )
Define a CAMEL agent helper class#
class CAMELAgent:
def __init__(
self,
system_message: SystemMessage,
model: ChatOpenAI,
) -> None:
self.system_message = system_message
self.model = model
self.init_messages()
def reset(self) -> None:
self.init_mess... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
f3aaf80350d4-3 | task_specifier_prompt = (
"""Here is a task that {assistant_role_name} will help {user_role_name} to complete: {task}.
Please make it more specific. Be creative and imaginative.
Please reply with the specified task in {word_limit} words or less. Do not add anything else."""
)
task_specifier_template = HumanMessagePromp... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
f3aaf80350d4-4 | assistant_inception_prompt = (
"""Never forget you are a {assistant_role_name} and I am a {user_role_name}. Never flip roles! Never instruct me!
We share a common interest in collaborating to successfully complete a task.
You must help me to complete the task.
Here is the task: {task}. Never forget our task!
I must ins... | /content/https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
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