mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
ed58eeb9c5
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
148 lines
5.0 KiB
Python
148 lines
5.0 KiB
Python
from typing import Any, Dict, List, Mapping, Optional, Tuple
|
|
|
|
import requests
|
|
from langchain_core.embeddings import Embeddings
|
|
from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
|
|
from langchain_core.utils import get_from_dict_or_env
|
|
|
|
|
|
class MosaicMLInstructorEmbeddings(BaseModel, Embeddings):
|
|
"""MosaicML embedding service.
|
|
|
|
To use, you should have the
|
|
environment variable ``MOSAICML_API_TOKEN`` set with your API token, or pass
|
|
it as a named parameter to the constructor.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.llms import MosaicMLInstructorEmbeddings
|
|
endpoint_url = (
|
|
"https://models.hosted-on.mosaicml.hosting/instructor-large/v1/predict"
|
|
)
|
|
mosaic_llm = MosaicMLInstructorEmbeddings(
|
|
endpoint_url=endpoint_url,
|
|
mosaicml_api_token="my-api-key"
|
|
)
|
|
"""
|
|
|
|
endpoint_url: str = (
|
|
"https://models.hosted-on.mosaicml.hosting/instructor-xl/v1/predict"
|
|
)
|
|
"""Endpoint URL to use."""
|
|
embed_instruction: str = "Represent the document for retrieval: "
|
|
"""Instruction used to embed documents."""
|
|
query_instruction: str = (
|
|
"Represent the question for retrieving supporting documents: "
|
|
)
|
|
"""Instruction used to embed the query."""
|
|
retry_sleep: float = 1.0
|
|
"""How long to try sleeping for if a rate limit is encountered"""
|
|
|
|
mosaicml_api_token: Optional[str] = None
|
|
|
|
class Config:
|
|
"""Configuration for this pydantic object."""
|
|
|
|
extra = Extra.forbid
|
|
|
|
@root_validator()
|
|
def validate_environment(cls, values: Dict) -> Dict:
|
|
"""Validate that api key and python package exists in environment."""
|
|
mosaicml_api_token = get_from_dict_or_env(
|
|
values, "mosaicml_api_token", "MOSAICML_API_TOKEN"
|
|
)
|
|
values["mosaicml_api_token"] = mosaicml_api_token
|
|
return values
|
|
|
|
@property
|
|
def _identifying_params(self) -> Mapping[str, Any]:
|
|
"""Get the identifying parameters."""
|
|
return {"endpoint_url": self.endpoint_url}
|
|
|
|
def _embed(
|
|
self, input: List[Tuple[str, str]], is_retry: bool = False
|
|
) -> List[List[float]]:
|
|
payload = {"inputs": input}
|
|
|
|
# HTTP headers for authorization
|
|
headers = {
|
|
"Authorization": f"{self.mosaicml_api_token}",
|
|
"Content-Type": "application/json",
|
|
}
|
|
|
|
# send request
|
|
try:
|
|
response = requests.post(self.endpoint_url, headers=headers, json=payload)
|
|
except requests.exceptions.RequestException as e:
|
|
raise ValueError(f"Error raised by inference endpoint: {e}")
|
|
|
|
try:
|
|
if response.status_code == 429:
|
|
if not is_retry:
|
|
import time
|
|
|
|
time.sleep(self.retry_sleep)
|
|
|
|
return self._embed(input, is_retry=True)
|
|
|
|
raise ValueError(
|
|
f"Error raised by inference API: rate limit exceeded.\nResponse: "
|
|
f"{response.text}"
|
|
)
|
|
|
|
parsed_response = response.json()
|
|
|
|
# The inference API has changed a couple of times, so we add some handling
|
|
# to be robust to multiple response formats.
|
|
if isinstance(parsed_response, dict):
|
|
output_keys = ["data", "output", "outputs"]
|
|
for key in output_keys:
|
|
if key in parsed_response:
|
|
output_item = parsed_response[key]
|
|
break
|
|
else:
|
|
raise ValueError(
|
|
f"No key data or output in response: {parsed_response}"
|
|
)
|
|
|
|
if isinstance(output_item, list) and isinstance(output_item[0], list):
|
|
embeddings = output_item
|
|
else:
|
|
embeddings = [output_item]
|
|
else:
|
|
raise ValueError(f"Unexpected response type: {parsed_response}")
|
|
|
|
except requests.exceptions.JSONDecodeError as e:
|
|
raise ValueError(
|
|
f"Error raised by inference API: {e}.\nResponse: {response.text}"
|
|
)
|
|
|
|
return embeddings
|
|
|
|
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
|
"""Embed documents using a MosaicML deployed instructor embedding model.
|
|
|
|
Args:
|
|
texts: The list of texts to embed.
|
|
|
|
Returns:
|
|
List of embeddings, one for each text.
|
|
"""
|
|
instruction_pairs = [(self.embed_instruction, text) for text in texts]
|
|
embeddings = self._embed(instruction_pairs)
|
|
return embeddings
|
|
|
|
def embed_query(self, text: str) -> List[float]:
|
|
"""Embed a query using a MosaicML deployed instructor embedding model.
|
|
|
|
Args:
|
|
text: The text to embed.
|
|
|
|
Returns:
|
|
Embeddings for the text.
|
|
"""
|
|
instruction_pair = (self.query_instruction, text)
|
|
embedding = self._embed([instruction_pair])[0]
|
|
return embedding
|