community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)

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
2025-09-06 21:43:44 +00:00 · 2023-12-11 13:53:30 -08:00
parent c0f4b95aa9
commit ed58eeb9c5
2446 changed files with 171805 additions and 137118 deletions
--- a/libs/community/langchain_community/llms/fireworks.py
+++ b/libs/community/langchain_community/llms/fireworks.py
@@ -0,0 +1,371 @@
+import asyncio
+from concurrent.futures import ThreadPoolExecutor
+from typing import Any, AsyncIterator, Callable, Dict, Iterator, List, Optional, Union
+
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain_core.language_models.llms import BaseLLM, create_base_retry_decorator
+from langchain_core.outputs import Generation, GenerationChunk, LLMResult
+from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
+from langchain_core.utils import convert_to_secret_str
+from langchain_core.utils.env import get_from_dict_or_env
+
+
+def _stream_response_to_generation_chunk(
+    stream_response: Any,
+) -> GenerationChunk:
+    """Convert a stream response to a generation chunk."""
+    return GenerationChunk(
+        text=stream_response.choices[0].text,
+        generation_info=dict(
+            finish_reason=stream_response.choices[0].finish_reason,
+            logprobs=stream_response.choices[0].logprobs,
+        ),
+    )
+
+
+class Fireworks(BaseLLM):
+    """Fireworks models."""
+
+    model: str = "accounts/fireworks/models/llama-v2-7b-chat"
+    model_kwargs: dict = Field(
+        default_factory=lambda: {
+            "temperature": 0.7,
+            "max_tokens": 512,
+            "top_p": 1,
+        }.copy()
+    )
+    fireworks_api_key: Optional[SecretStr] = None
+    max_retries: int = 20
+    batch_size: int = 20
+    use_retry: bool = True
+
+    @property
+    def lc_secrets(self) -> Dict[str, str]:
+        return {"fireworks_api_key": "FIREWORKS_API_KEY"}
+
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return True
+
+    @classmethod
+    def get_lc_namespace(cls) -> List[str]:
+        """Get the namespace of the langchain object."""
+        return ["langchain", "llms", "fireworks"]
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key in environment."""
+        try:
+            import fireworks.client
+        except ImportError as e:
+            raise ImportError(
+                "Could not import fireworks-ai python package. "
+                "Please install it with `pip install fireworks-ai`."
+            ) from e
+        fireworks_api_key = convert_to_secret_str(
+            get_from_dict_or_env(values, "fireworks_api_key", "FIREWORKS_API_KEY")
+        )
+        fireworks.client.api_key = fireworks_api_key.get_secret_value()
+        return values
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "fireworks"
+
+    def _generate(
+        self,
+        prompts: List[str],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> LLMResult:
+        """Call out to Fireworks endpoint with k unique prompts.
+        Args:
+            prompts: The prompts to pass into the model.
+            stop: Optional list of stop words to use when generating.
+        Returns:
+            The full LLM output.
+        """
+        params = {
+            "model": self.model,
+            **self.model_kwargs,
+        }
+        sub_prompts = self.get_batch_prompts(prompts)
+        choices = []
+        for _prompts in sub_prompts:
+            response = completion_with_retry_batching(
+                self,
+                self.use_retry,
+                prompt=_prompts,
+                run_manager=run_manager,
+                stop=stop,
+                **params,
+            )
+            choices.extend(response)
+
+        return self.create_llm_result(choices, prompts)
+
+    async def _agenerate(
+        self,
+        prompts: List[str],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> LLMResult:
+        """Call out to Fireworks endpoint async with k unique prompts."""
+        params = {
+            "model": self.model,
+            **self.model_kwargs,
+        }
+        sub_prompts = self.get_batch_prompts(prompts)
+        choices = []
+        for _prompts in sub_prompts:
+            response = await acompletion_with_retry_batching(
+                self,
+                self.use_retry,
+                prompt=_prompts,
+                run_manager=run_manager,
+                stop=stop,
+                **params,
+            )
+            choices.extend(response)
+
+        return self.create_llm_result(choices, prompts)
+
+    def get_batch_prompts(
+        self,
+        prompts: List[str],
+    ) -> List[List[str]]:
+        """Get the sub prompts for llm call."""
+        sub_prompts = [
+            prompts[i : i + self.batch_size]
+            for i in range(0, len(prompts), self.batch_size)
+        ]
+        return sub_prompts
+
+    def create_llm_result(self, choices: Any, prompts: List[str]) -> LLMResult:
+        """Create the LLMResult from the choices and prompts."""
+        generations = []
+        for i, _ in enumerate(prompts):
+            sub_choices = choices[i : (i + 1)]
+            generations.append(
+                [
+                    Generation(
+                        text=choice.__dict__["choices"][0].text,
+                    )
+                    for choice in sub_choices
+                ]
+            )
+        llm_output = {"model": self.model}
+        return LLMResult(generations=generations, llm_output=llm_output)
+
+    def _stream(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[GenerationChunk]:
+        params = {
+            "model": self.model,
+            "prompt": prompt,
+            "stream": True,
+            **self.model_kwargs,
+        }
+        for stream_resp in completion_with_retry(
+            self, self.use_retry, run_manager=run_manager, stop=stop, **params
+        ):
+            chunk = _stream_response_to_generation_chunk(stream_resp)
+            yield chunk
+            if run_manager:
+                run_manager.on_llm_new_token(chunk.text, chunk=chunk)
+
+    async def _astream(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[GenerationChunk]:
+        params = {
+            "model": self.model,
+            "prompt": prompt,
+            "stream": True,
+            **self.model_kwargs,
+        }
+        async for stream_resp in await acompletion_with_retry_streaming(
+            self, self.use_retry, run_manager=run_manager, stop=stop, **params
+        ):
+            chunk = _stream_response_to_generation_chunk(stream_resp)
+            yield chunk
+            if run_manager:
+                await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
+
+
+def conditional_decorator(
+    condition: bool, decorator: Callable[[Any], Any]
+) -> Callable[[Any], Any]:
+    def actual_decorator(func: Callable[[Any], Any]) -> Callable[[Any], Any]:
+        if condition:
+            return decorator(func)
+        return func
+
+    return actual_decorator
+
+
+def completion_with_retry(
+    llm: Fireworks,
+    use_retry: bool,
+    *,
+    run_manager: Optional[CallbackManagerForLLMRun] = None,
+    **kwargs: Any,
+) -> Any:
+    """Use tenacity to retry the completion call."""
+    import fireworks.client
+
+    retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
+
+    @conditional_decorator(use_retry, retry_decorator)
+    def _completion_with_retry(**kwargs: Any) -> Any:
+        return fireworks.client.Completion.create(
+            **kwargs,
+        )
+
+    return _completion_with_retry(**kwargs)
+
+
+async def acompletion_with_retry(
+    llm: Fireworks,
+    use_retry: bool,
+    *,
+    run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+    **kwargs: Any,
+) -> Any:
+    """Use tenacity to retry the completion call."""
+    import fireworks.client
+
+    retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
+
+    @conditional_decorator(use_retry, retry_decorator)
+    async def _completion_with_retry(**kwargs: Any) -> Any:
+        return await fireworks.client.Completion.acreate(
+            **kwargs,
+        )
+
+    return await _completion_with_retry(**kwargs)
+
+
+def completion_with_retry_batching(
+    llm: Fireworks,
+    use_retry: bool,
+    *,
+    run_manager: Optional[CallbackManagerForLLMRun] = None,
+    **kwargs: Any,
+) -> Any:
+    """Use tenacity to retry the completion call."""
+    import fireworks.client
+
+    prompt = kwargs["prompt"]
+    del kwargs["prompt"]
+
+    retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
+
+    @conditional_decorator(use_retry, retry_decorator)
+    def _completion_with_retry(prompt: str) -> Any:
+        return fireworks.client.Completion.create(**kwargs, prompt=prompt)
+
+    def batch_sync_run() -> List:
+        with ThreadPoolExecutor() as executor:
+            results = list(executor.map(_completion_with_retry, prompt))
+        return results
+
+    return batch_sync_run()
+
+
+async def acompletion_with_retry_batching(
+    llm: Fireworks,
+    use_retry: bool,
+    *,
+    run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+    **kwargs: Any,
+) -> Any:
+    """Use tenacity to retry the completion call."""
+    import fireworks.client
+
+    prompt = kwargs["prompt"]
+    del kwargs["prompt"]
+
+    retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
+
+    @conditional_decorator(use_retry, retry_decorator)
+    async def _completion_with_retry(prompt: str) -> Any:
+        return await fireworks.client.Completion.acreate(**kwargs, prompt=prompt)
+
+    def run_coroutine_in_new_loop(
+        coroutine_func: Any, *args: Dict, **kwargs: Dict
+    ) -> Any:
+        new_loop = asyncio.new_event_loop()
+        try:
+            asyncio.set_event_loop(new_loop)
+            return new_loop.run_until_complete(coroutine_func(*args, **kwargs))
+        finally:
+            new_loop.close()
+
+    async def batch_sync_run() -> List:
+        with ThreadPoolExecutor() as executor:
+            results = list(
+                executor.map(
+                    run_coroutine_in_new_loop,
+                    [_completion_with_retry] * len(prompt),
+                    prompt,
+                )
+            )
+        return results
+
+    return await batch_sync_run()
+
+
+async def acompletion_with_retry_streaming(
+    llm: Fireworks,
+    use_retry: bool,
+    *,
+    run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+    **kwargs: Any,
+) -> Any:
+    """Use tenacity to retry the completion call for streaming."""
+    import fireworks.client
+
+    retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
+
+    @conditional_decorator(use_retry, retry_decorator)
+    async def _completion_with_retry(**kwargs: Any) -> Any:
+        return fireworks.client.Completion.acreate(
+            **kwargs,
+        )
+
+    return await _completion_with_retry(**kwargs)
+
+
+def _create_retry_decorator(
+    llm: Fireworks,
+    *,
+    run_manager: Optional[
+        Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
+    ] = None,
+) -> Callable[[Any], Any]:
+    """Define retry mechanism."""
+    import fireworks.client
+
+    errors = [
+        fireworks.client.error.RateLimitError,
+        fireworks.client.error.InternalServerError,
+        fireworks.client.error.BadGatewayError,
+        fireworks.client.error.ServiceUnavailableError,
+    ]
+    return create_base_retry_decorator(
+        error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
+    )