mirror of
https://github.com/hwchase17/langchain
synced 2024-11-18 09:25:54 +00:00
219 lines
7.5 KiB
Python
219 lines
7.5 KiB
Python
import logging
|
|
from typing import Any, Dict, List, Mapping, Optional
|
|
|
|
import requests
|
|
from langchain_core.embeddings import Embeddings
|
|
from langchain_core.pydantic_v1 import BaseModel, Extra
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class OllamaEmbeddings(BaseModel, Embeddings):
|
|
"""Ollama locally runs large language models.
|
|
|
|
To use, follow the instructions at https://ollama.ai/.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.embeddings import OllamaEmbeddings
|
|
ollama_emb = OllamaEmbeddings(
|
|
model="llama:7b",
|
|
)
|
|
r1 = ollama_emb.embed_documents(
|
|
[
|
|
"Alpha is the first letter of Greek alphabet",
|
|
"Beta is the second letter of Greek alphabet",
|
|
]
|
|
)
|
|
r2 = ollama_emb.embed_query(
|
|
"What is the second letter of Greek alphabet"
|
|
)
|
|
|
|
"""
|
|
|
|
base_url: str = "http://localhost:11434"
|
|
"""Base url the model is hosted under."""
|
|
model: str = "llama2"
|
|
"""Model name to use."""
|
|
|
|
embed_instruction: str = "passage: "
|
|
"""Instruction used to embed documents."""
|
|
query_instruction: str = "query: "
|
|
"""Instruction used to embed the query."""
|
|
|
|
mirostat: Optional[int] = None
|
|
"""Enable Mirostat sampling for controlling perplexity.
|
|
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"""
|
|
|
|
mirostat_eta: Optional[float] = None
|
|
"""Influences how quickly the algorithm responds to feedback
|
|
from the generated text. A lower learning rate will result in
|
|
slower adjustments, while a higher learning rate will make
|
|
the algorithm more responsive. (Default: 0.1)"""
|
|
|
|
mirostat_tau: Optional[float] = None
|
|
"""Controls the balance between coherence and diversity
|
|
of the output. A lower value will result in more focused and
|
|
coherent text. (Default: 5.0)"""
|
|
|
|
num_ctx: Optional[int] = None
|
|
"""Sets the size of the context window used to generate the
|
|
next token. (Default: 2048) """
|
|
|
|
num_gpu: Optional[int] = None
|
|
"""The number of GPUs to use. On macOS it defaults to 1 to
|
|
enable metal support, 0 to disable."""
|
|
|
|
num_thread: Optional[int] = None
|
|
"""Sets the number of threads to use during computation.
|
|
By default, Ollama will detect this for optimal performance.
|
|
It is recommended to set this value to the number of physical
|
|
CPU cores your system has (as opposed to the logical number of cores)."""
|
|
|
|
repeat_last_n: Optional[int] = None
|
|
"""Sets how far back for the model to look back to prevent
|
|
repetition. (Default: 64, 0 = disabled, -1 = num_ctx)"""
|
|
|
|
repeat_penalty: Optional[float] = None
|
|
"""Sets how strongly to penalize repetitions. A higher value (e.g., 1.5)
|
|
will penalize repetitions more strongly, while a lower value (e.g., 0.9)
|
|
will be more lenient. (Default: 1.1)"""
|
|
|
|
temperature: Optional[float] = None
|
|
"""The temperature of the model. Increasing the temperature will
|
|
make the model answer more creatively. (Default: 0.8)"""
|
|
|
|
stop: Optional[List[str]] = None
|
|
"""Sets the stop tokens to use."""
|
|
|
|
tfs_z: Optional[float] = None
|
|
"""Tail free sampling is used to reduce the impact of less probable
|
|
tokens from the output. A higher value (e.g., 2.0) will reduce the
|
|
impact more, while a value of 1.0 disables this setting. (default: 1)"""
|
|
|
|
top_k: Optional[int] = None
|
|
"""Reduces the probability of generating nonsense. A higher value (e.g. 100)
|
|
will give more diverse answers, while a lower value (e.g. 10)
|
|
will be more conservative. (Default: 40)"""
|
|
|
|
top_p: Optional[float] = None
|
|
"""Works together with top-k. A higher value (e.g., 0.95) will lead
|
|
to more diverse text, while a lower value (e.g., 0.5) will
|
|
generate more focused and conservative text. (Default: 0.9)"""
|
|
|
|
show_progress: bool = False
|
|
"""Whether to show a tqdm progress bar. Must have `tqdm` installed."""
|
|
|
|
@property
|
|
def _default_params(self) -> Dict[str, Any]:
|
|
"""Get the default parameters for calling Ollama."""
|
|
return {
|
|
"model": self.model,
|
|
"options": {
|
|
"mirostat": self.mirostat,
|
|
"mirostat_eta": self.mirostat_eta,
|
|
"mirostat_tau": self.mirostat_tau,
|
|
"num_ctx": self.num_ctx,
|
|
"num_gpu": self.num_gpu,
|
|
"num_thread": self.num_thread,
|
|
"repeat_last_n": self.repeat_last_n,
|
|
"repeat_penalty": self.repeat_penalty,
|
|
"temperature": self.temperature,
|
|
"stop": self.stop,
|
|
"tfs_z": self.tfs_z,
|
|
"top_k": self.top_k,
|
|
"top_p": self.top_p,
|
|
},
|
|
}
|
|
|
|
model_kwargs: Optional[dict] = None
|
|
"""Other model keyword args"""
|
|
|
|
@property
|
|
def _identifying_params(self) -> Mapping[str, Any]:
|
|
"""Get the identifying parameters."""
|
|
return {**{"model": self.model}, **self._default_params}
|
|
|
|
class Config:
|
|
"""Configuration for this pydantic object."""
|
|
|
|
extra = Extra.forbid
|
|
|
|
def _process_emb_response(self, input: str) -> List[float]:
|
|
"""Process a response from the API.
|
|
|
|
Args:
|
|
response: The response from the API.
|
|
|
|
Returns:
|
|
The response as a dictionary.
|
|
"""
|
|
headers = {
|
|
"Content-Type": "application/json",
|
|
}
|
|
|
|
try:
|
|
res = requests.post(
|
|
f"{self.base_url}/api/embeddings",
|
|
headers=headers,
|
|
json={"model": self.model, "prompt": input, **self._default_params},
|
|
)
|
|
except requests.exceptions.RequestException as e:
|
|
raise ValueError(f"Error raised by inference endpoint: {e}")
|
|
|
|
if res.status_code != 200:
|
|
raise ValueError(
|
|
"Error raised by inference API HTTP code: %s, %s"
|
|
% (res.status_code, res.text)
|
|
)
|
|
try:
|
|
t = res.json()
|
|
return t["embedding"]
|
|
except requests.exceptions.JSONDecodeError as e:
|
|
raise ValueError(
|
|
f"Error raised by inference API: {e}.\nResponse: {res.text}"
|
|
)
|
|
|
|
def _embed(self, input: List[str]) -> List[List[float]]:
|
|
if self.show_progress:
|
|
try:
|
|
from tqdm import tqdm
|
|
|
|
iter_ = tqdm(input, desc="OllamaEmbeddings")
|
|
except ImportError:
|
|
logger.warning(
|
|
"Unable to show progress bar because tqdm could not be imported. "
|
|
"Please install with `pip install tqdm`."
|
|
)
|
|
iter_ = input
|
|
else:
|
|
iter_ = input
|
|
return [self._process_emb_response(prompt) for prompt in iter_]
|
|
|
|
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
|
"""Embed documents using an Ollama deployed embedding model.
|
|
|
|
Args:
|
|
texts: The list of texts to embed.
|
|
|
|
Returns:
|
|
List of embeddings, one for each text.
|
|
"""
|
|
instruction_pairs = [f"{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 Ollama deployed embedding model.
|
|
|
|
Args:
|
|
text: The text to embed.
|
|
|
|
Returns:
|
|
Embeddings for the text.
|
|
"""
|
|
instruction_pair = f"{self.query_instruction}{text}"
|
|
embedding = self._embed([instruction_pair])[0]
|
|
return embedding
|