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[docs]: Revamp embedchain docs (#799)

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This commit is contained in:
Deshraj Yadav
2023-10-13 15:38:15 -07:00
committed by GitHub
parent a86d7f52e9
commit 4a8c50f886
68 changed files with 1175 additions and 673 deletions
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10
embedchain/llm/base.py
View File

@@ -5,9 +5,9 @@ from langchain.memory import ConversationBufferMemory
from langchain.schema import BaseMessage
from embedchain.config import BaseLlmConfig
from embedchain.config.llm.base_llm_config import (
DEFAULT_PROMPT, DEFAULT_PROMPT_WITH_HISTORY_TEMPLATE,
DOCS_SITE_PROMPT_TEMPLATE)
from embedchain.config.llm.base import (DEFAULT_PROMPT,
DEFAULT_PROMPT_WITH_HISTORY_TEMPLATE,
DOCS_SITE_PROMPT_TEMPLATE)
from embedchain.helper.json_serializable import JSONSerializable
@@ -174,7 +174,7 @@ class BaseLlm(JSONSerializable):
:type input_query: str
:param contexts: Embeddings retrieved from the database to be used as context.
:type contexts: List[str]
:param config: The `LlmConfig` instance to use as configuration options. This is used for one method call.
:param config: The `BaseLlmConfig` instance to use as configuration options. This is used for one method call.
To persistently use a config, declare it during app init., defaults to None
:type config: Optional[BaseLlmConfig], optional
:param dry_run: A dry run does everything except send the resulting prompt to
@@ -230,7 +230,7 @@ class BaseLlm(JSONSerializable):
:type input_query: str
:param contexts: Embeddings retrieved from the database to be used as context.
:type contexts: List[str]
:param config: The `LlmConfig` instance to use as configuration options. This is used for one method call.
:param config: The `BaseLlmConfig` instance to use as configuration options. This is used for one method call.
To persistently use a config, declare it during app init., defaults to None
:type config: Optional[BaseLlmConfig], optional
:param dry_run: A dry run does everything except send the resulting prompt to

4
embedchain/llm/gpt4all.py
View File

@@ -30,11 +30,11 @@ class GPT4ALLLlm(BaseLlm):
def _get_answer(self, prompt: str, config: BaseLlmConfig) -> Union[str, Iterable]:
if config.model and config.model != self.config.model:
raise RuntimeError(
"OpenSourceApp does not support switching models at runtime. Please create a new app instance."
"GPT4ALLLlm does not support switching models at runtime. Please create a new app instance."
)
if config.system_prompt:
raise ValueError("OpenSourceApp does not support `system_prompt`")
raise ValueError("GPT4ALLLlm does not support `system_prompt`")
response = self.instance.generate(
prompt=prompt,

12
embedchain/llm/hugging_face_hub.py → embedchain/llm/huggingface.py
View File

@@ -10,10 +10,10 @@ from embedchain.llm.base import BaseLlm
@register_deserializable
class HuggingFaceHubLlm(BaseLlm):
class HuggingFaceLlm(BaseLlm):
def __init__(self, config: Optional[BaseLlmConfig] = None):
if "HUGGINGFACEHUB_ACCESS_TOKEN" not in os.environ:
raise ValueError("Please set the HUGGINGFACEHUB_ACCESS_TOKEN environment variable.")
if "HUGGINGFACE_ACCESS_TOKEN" not in os.environ:
raise ValueError("Please set the HUGGINGFACE_ACCESS_TOKEN environment variable.")
try:
importlib.import_module("huggingface_hub")
@@ -27,8 +27,8 @@ class HuggingFaceHubLlm(BaseLlm):
def get_llm_model_answer(self, prompt):
if self.config.system_prompt:
raise ValueError("HuggingFaceHubLlm does not support `system_prompt`")
return HuggingFaceHubLlm._get_answer(prompt=prompt, config=self.config)
raise ValueError("HuggingFaceLlm does not support `system_prompt`")
return HuggingFaceLlm._get_answer(prompt=prompt, config=self.config)
@staticmethod
def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
@@ -43,7 +43,7 @@ class HuggingFaceHubLlm(BaseLlm):
raise ValueError("`top_p` must be > 0.0 and < 1.0")
llm = HuggingFaceHub(
huggingfacehub_api_token=os.environ["HUGGINGFACEHUB_ACCESS_TOKEN"],
huggingfacehub_api_token=os.environ["HUGGINGFACE_ACCESS_TOKEN"],
repo_id=config.model or "google/flan-t5-xxl",
model_kwargs=model_kwargs,
)

4
embedchain/llm/vertex_ai.py
View File

@@ -7,12 +7,12 @@ from embedchain.llm.base import BaseLlm
@register_deserializable
class VertexAiLlm(BaseLlm):
class VertexAILlm(BaseLlm):
def __init__(self, config: Optional[BaseLlmConfig] = None):
super().__init__(config=config)
def get_llm_model_answer(self, prompt):
return VertexAiLlm._get_answer(prompt=prompt, config=self.config)
return VertexAILlm._get_answer(prompt=prompt, config=self.config)
@staticmethod
def _get_answer(prompt: str, config: BaseLlmConfig) -> str: