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__pycache__ |
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MIT License | ||
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Copyright (c) 2023 LangChain, Inc. | ||
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Permission is hereby granted, free of charge, to any person obtaining a copy | ||
of this software and associated documentation files (the "Software"), to deal | ||
in the Software without restriction, including without limitation the rights | ||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
copies of the Software, and to permit persons to whom the Software is | ||
furnished to do so, subject to the following conditions: | ||
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The above copyright notice and this permission notice shall be included in all | ||
copies or substantial portions of the Software. | ||
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
SOFTWARE. |
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.PHONY: all format lint test tests integration_tests docker_tests help extended_tests | ||
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# Default target executed when no arguments are given to make. | ||
all: help | ||
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# Define a variable for the test file path. | ||
TEST_FILE ?= tests/unit_tests/ | ||
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test: | ||
poetry run pytest $(TEST_FILE) | ||
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tests: | ||
poetry run pytest $(TEST_FILE) | ||
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###################### | ||
# LINTING AND FORMATTING | ||
###################### | ||
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# Define a variable for Python and notebook files. | ||
PYTHON_FILES=. | ||
MYPY_CACHE=.mypy_cache | ||
lint format: PYTHON_FILES=. | ||
lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/fireworks --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') | ||
lint_package: PYTHON_FILES=langchain_fireworks | ||
lint_tests: PYTHON_FILES=tests | ||
lint_tests: MYPY_CACHE=.mypy_cache_test | ||
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lint lint_diff lint_package lint_tests: | ||
poetry run ruff . | ||
poetry run ruff format $(PYTHON_FILES) --diff | ||
poetry run ruff --select I $(PYTHON_FILES) | ||
mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) | ||
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format format_diff: | ||
poetry run ruff format $(PYTHON_FILES) | ||
poetry run ruff --select I --fix $(PYTHON_FILES) | ||
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spell_check: | ||
poetry run codespell --toml pyproject.toml | ||
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spell_fix: | ||
poetry run codespell --toml pyproject.toml -w | ||
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check_imports: $(shell find langchain_fireworks -name '*.py') | ||
poetry run python ./scripts/check_imports.py $^ | ||
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###################### | ||
# HELP | ||
###################### | ||
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help: | ||
@echo '----' | ||
@echo 'check_imports - check imports' | ||
@echo 'format - run code formatters' | ||
@echo 'lint - run linters' | ||
@echo 'test - run unit tests' | ||
@echo 'tests - run unit tests' | ||
@echo 'test TEST_FILE=<test_file> - run all tests in file' |
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# LangChain-Fireworks | ||
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This is the partner package for tying Fireworks.ai and LangChain together |
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from langchain_fireworks.embeddings import FireworksEmbeddings | ||
from langchain_fireworks.llms import Fireworks | ||
from langchain_fireworks.version import __version__ | ||
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__all__ = [ | ||
"__version__", | ||
"Fireworks", | ||
"FireworksEmbeddings", | ||
] |
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import os | ||
from typing import Any, Dict, List | ||
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import fireworks # type: ignore | ||
from langchain_core.embeddings import Embeddings | ||
from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator | ||
from langchain_core.utils import convert_to_secret_str | ||
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class FireworksEmbeddings(BaseModel, Embeddings): | ||
"""FireworksEmbeddings embedding model. | ||
Example: | ||
.. code-block:: python | ||
from langchain_fireworks import FireworksEmbeddings | ||
model = FireworksEmbeddings( | ||
model='nomic-ai/nomic-embed-text-v1.5' | ||
) | ||
""" | ||
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_client: fireworks.client.Fireworks | ||
fireworks_api_key: SecretStr = convert_to_secret_str("") | ||
model: str | ||
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@root_validator() | ||
def validate_environment(cls, values: Dict[str, Any]) -> Dict[str, Any]: | ||
"""Validate environment variables.""" | ||
fireworks_api_key = convert_to_secret_str( | ||
values.get("fireworks_api_key") or os.getenv("FIREWORKS_API_KEY") or "" | ||
) | ||
values["fireworks_api_key"] = fireworks_api_key | ||
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# note this sets it globally for module | ||
# there isn't currently a way to pass it into client | ||
fireworks.api_key = fireworks_api_key.get_secret_value() | ||
values["_client"] = fireworks.client.Fireworks() | ||
return values | ||
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def embed_documents(self, texts: List[str]) -> List[List[float]]: | ||
"""Embed search docs.""" | ||
return [ | ||
i.embedding | ||
for i in self._client.embeddings.create(input=texts, model=self.model).data | ||
] | ||
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def embed_query(self, text: str) -> List[float]: | ||
"""Embed query text.""" | ||
return self.embed_documents([text])[0] |
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"""Wrapper around Fireworks AI's Completion API.""" | ||
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import logging | ||
from typing import Any, Dict, List, Optional | ||
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import requests | ||
from aiohttp import ClientSession | ||
from langchain_core.callbacks import ( | ||
AsyncCallbackManagerForLLMRun, | ||
CallbackManagerForLLMRun, | ||
) | ||
from langchain_core.language_models.llms import LLM | ||
from langchain_core.pydantic_v1 import Extra, SecretStr, root_validator | ||
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env | ||
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from langchain_fireworks.version import __version__ | ||
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logger = logging.getLogger(__name__) | ||
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class Fireworks(LLM): | ||
"""LLM models from `Fireworks`. | ||
To use, you'll need an API key which you can find here: | ||
https://fireworks.ai This can be passed in as init param | ||
``fireworks_api_key`` or set as environment variable ``FIREWORKS_API_KEY``. | ||
Fireworks AI API reference: https://readme.fireworks.ai/ | ||
""" | ||
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base_url: str = "https://api.fireworks.ai/inference/v1" | ||
"""Base inference API URL.""" | ||
fireworks_api_key: SecretStr | ||
"""Fireworks AI API key. Get it here: https://fireworks.ai""" | ||
model: str | ||
"""Model name. Available models listed here: | ||
https://readme.fireworks.ai/ | ||
""" | ||
temperature: Optional[float] = None | ||
"""Model temperature.""" | ||
top_p: Optional[float] = None | ||
"""Used to dynamically adjust the number of choices for each predicted token based | ||
on the cumulative probabilities. A value of 1 will always yield the same | ||
output. A temperature less than 1 favors more correctness and is appropriate | ||
for question answering or summarization. A value greater than 1 introduces more | ||
randomness in the output. | ||
""" | ||
top_k: Optional[int] = None | ||
"""Used to limit the number of choices for the next predicted word or token. It | ||
specifies the maximum number of tokens to consider at each step, based on their | ||
probability of occurrence. This technique helps to speed up the generation | ||
process and can improve the quality of the generated text by focusing on the | ||
most likely options. | ||
""" | ||
max_tokens: Optional[int] = None | ||
"""The maximum number of tokens to generate.""" | ||
repetition_penalty: Optional[float] = None | ||
"""A number that controls the diversity of generated text by reducing the | ||
likelihood of repeated sequences. Higher values decrease repetition. | ||
""" | ||
logprobs: Optional[int] = None | ||
"""An integer that specifies how many top token log probabilities are included in | ||
the response for each token generation step. | ||
""" | ||
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class Config: | ||
"""Configuration for this pydantic object.""" | ||
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extra = Extra.forbid | ||
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@root_validator(pre=True) | ||
def validate_environment(cls, values: Dict) -> Dict: | ||
"""Validate that api key exists in environment.""" | ||
values["fireworks_api_key"] = convert_to_secret_str( | ||
get_from_dict_or_env(values, "fireworks_api_key", "FIREWORKS_API_KEY") | ||
) | ||
return values | ||
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@property | ||
def _llm_type(self) -> str: | ||
"""Return type of model.""" | ||
return "fireworks" | ||
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def _format_output(self, output: dict) -> str: | ||
return output["output"]["choices"][0]["text"] | ||
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@staticmethod | ||
def get_user_agent() -> str: | ||
return f"langchain-fireworks/{__version__}" | ||
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@property | ||
def default_params(self) -> Dict[str, Any]: | ||
return { | ||
"model": self.model, | ||
"temperature": self.temperature, | ||
"top_p": self.top_p, | ||
"top_k": self.top_k, | ||
"max_tokens": self.max_tokens, | ||
"repetition_penalty": self.repetition_penalty, | ||
} | ||
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def _call( | ||
self, | ||
prompt: str, | ||
stop: Optional[List[str]] = None, | ||
run_manager: Optional[CallbackManagerForLLMRun] = None, | ||
**kwargs: Any, | ||
) -> str: | ||
"""Call out to Fireworks's text generation endpoint. | ||
Args: | ||
prompt: The prompt to pass into the model. | ||
Returns: | ||
The string generated by the model.. | ||
""" | ||
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headers = { | ||
"Authorization": f"Bearer {self.fireworks_api_key.get_secret_value()}", | ||
"Content-Type": "application/json", | ||
} | ||
stop_to_use = stop[0] if stop and len(stop) == 1 else stop | ||
payload: Dict[str, Any] = { | ||
**self.default_params, | ||
"prompt": prompt, | ||
"stop": stop_to_use, | ||
**kwargs, | ||
} | ||
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# filter None values to not pass them to the http payload | ||
payload = {k: v for k, v in payload.items() if v is not None} | ||
response = requests.post(url=self.base_url, json=payload, headers=headers) | ||
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if response.status_code >= 500: | ||
raise Exception(f"Fireworks Server: Error {response.status_code}") | ||
elif response.status_code >= 400: | ||
raise ValueError(f"Fireworks received an invalid payload: {response.text}") | ||
elif response.status_code != 200: | ||
raise Exception( | ||
f"Fireworks returned an unexpected response with status " | ||
f"{response.status_code}: {response.text}" | ||
) | ||
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data = response.json() | ||
if data.get("status") != "finished": | ||
err_msg = data.get("error", "Undefined Error") | ||
raise Exception(err_msg) | ||
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output = self._format_output(data) | ||
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return output | ||
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async def _acall( | ||
self, | ||
prompt: str, | ||
stop: Optional[List[str]] = None, | ||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, | ||
**kwargs: Any, | ||
) -> str: | ||
"""Call Fireworks model to get predictions based on the prompt. | ||
Args: | ||
prompt: The prompt to pass into the model. | ||
Returns: | ||
The string generated by the model. | ||
""" | ||
headers = { | ||
"Authorization": f"Bearer {self.fireworks_api_key.get_secret_value()}", | ||
"Content-Type": "application/json", | ||
} | ||
stop_to_use = stop[0] if stop and len(stop) == 1 else stop | ||
payload: Dict[str, Any] = { | ||
**self.default_params, | ||
"prompt": prompt, | ||
"stop": stop_to_use, | ||
**kwargs, | ||
} | ||
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# filter None values to not pass them to the http payload | ||
payload = {k: v for k, v in payload.items() if v is not None} | ||
async with ClientSession() as session: | ||
async with session.post( | ||
self.base_url, json=payload, headers=headers | ||
) as response: | ||
if response.status >= 500: | ||
raise Exception(f"Fireworks Server: Error {response.status}") | ||
elif response.status >= 400: | ||
raise ValueError( | ||
f"Fireworks received an invalid payload: {response.text}" | ||
) | ||
elif response.status != 200: | ||
raise Exception( | ||
f"Fireworks returned an unexpected response with status " | ||
f"{response.status}: {response.text}" | ||
) | ||
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response_json = await response.json() | ||
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if response_json.get("status") != "finished": | ||
err_msg = response_json.get("error", "Undefined Error") | ||
raise Exception(err_msg) | ||
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output = self._format_output(response_json) | ||
return output |
Empty file.
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"""Main entrypoint into package.""" | ||
from importlib import metadata | ||
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try: | ||
__version__ = metadata.version(__package__) | ||
except metadata.PackageNotFoundError: | ||
# Case where package metadata is not available. | ||
__version__ = "" |
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