Extract completion provider crate (#14823)
We will soon need `semantic_index` to be able to use `CompletionProvider`. This is currently impossible due to a cyclic crate dependency, because `CompletionProvider` lives in the `assistant` crate, which depends on `semantic_index`. This PR breaks the dependency cycle by extracting two crates out of `assistant`: `language_model` and `completion`. Only one piece of logic changed: [this code](https://github.com/zed-industries/zed/commit/922fcaf5a6076e56890373035b1065b13512546d#diff-3857b3707687a4d585f1200eec4c34a7a079eae8d303b4ce5b4fce46234ace9fR61-R69). * As of https://github.com/zed-industries/zed/pull/13276, whenever we ask a given completion provider for its available models, OpenAI providers would go and ask the global assistant settings whether the user had configured an `available_models` setting, and if so, return that. * This PR changes it so that instead of eagerly asking the assistant settings for this info (the new crate must not depend on `assistant`, or else the dependency cycle would be back), OpenAI completion providers now store the user-configured settings as part of their struct, and whenever the settings change, we update the provider. In theory, this change should not change user-visible behavior...but since it's the only change in this large PR that's more than just moving code around, I'm mentioning it here in case there's an unexpected regression in practice! (cc @amtoaer in case you'd like to try out this branch and verify that the feature is still working the way you expect.) Release Notes: - N/A --------- Co-authored-by: Marshall Bowers <elliott.codes@gmail.com>
This commit is contained in:
Richard Feldman
co-authored by
Marshall Bowers
Marshall Bowers
parent
b9a53ffa0b
commit
ec487d8f64
@@ -0,0 +1,160 @@
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use crate::LanguageModelRequest;
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pub use anthropic::Model as AnthropicModel;
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pub use ollama::Model as OllamaModel;
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pub use open_ai::Model as OpenAiModel;
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use schemars::{
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schema::{InstanceType, Metadata, Schema, SchemaObject},
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JsonSchema,
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};
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use serde::{
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de::{self, Visitor},
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Deserialize, Deserializer, Serialize, Serializer,
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};
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use std::fmt;
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use strum::{EnumIter, IntoEnumIterator};
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#[derive(Clone, Debug, Default, PartialEq, EnumIter)]
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pub enum CloudModel {
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Gpt3Point5Turbo,
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Gpt4,
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Gpt4Turbo,
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#[default]
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Gpt4Omni,
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Gpt4OmniMini,
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Claude3_5Sonnet,
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Claude3Opus,
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Claude3Sonnet,
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Claude3Haiku,
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Gemini15Pro,
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Gemini15Flash,
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Custom(String),
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}
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impl Serialize for CloudModel {
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fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
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where
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S: Serializer,
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{
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serializer.serialize_str(self.id())
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}
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}
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impl<'de> Deserialize<'de> for CloudModel {
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fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
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where
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D: Deserializer<'de>,
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{
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struct ZedDotDevModelVisitor;
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impl<'de> Visitor<'de> for ZedDotDevModelVisitor {
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type Value = CloudModel;
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fn expecting(&self, formatter: &mut fmt::Formatter) -> fmt::Result {
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formatter.write_str("a string for a ZedDotDevModel variant or a custom model")
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}
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fn visit_str<E>(self, value: &str) -> Result<Self::Value, E>
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where
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E: de::Error,
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{
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let model = CloudModel::iter()
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.find(|model| model.id() == value)
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.unwrap_or_else(|| CloudModel::Custom(value.to_string()));
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Ok(model)
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}
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}
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deserializer.deserialize_str(ZedDotDevModelVisitor)
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}
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}
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impl JsonSchema for CloudModel {
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fn schema_name() -> String {
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"ZedDotDevModel".to_owned()
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}
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fn json_schema(_generator: &mut schemars::gen::SchemaGenerator) -> Schema {
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let variants = CloudModel::iter()
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.filter_map(|model| {
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let id = model.id();
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if id.is_empty() {
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None
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} else {
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Some(id.to_string())
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}
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})
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.collect::<Vec<_>>();
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Schema::Object(SchemaObject {
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instance_type: Some(InstanceType::String.into()),
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enum_values: Some(variants.iter().map(|s| s.clone().into()).collect()),
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metadata: Some(Box::new(Metadata {
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title: Some("ZedDotDevModel".to_owned()),
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default: Some(CloudModel::default().id().into()),
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examples: variants.into_iter().map(Into::into).collect(),
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..Default::default()
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})),
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..Default::default()
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})
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}
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}
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impl CloudModel {
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pub fn id(&self) -> &str {
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match self {
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Self::Gpt3Point5Turbo => "gpt-3.5-turbo",
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Self::Gpt4 => "gpt-4",
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Self::Gpt4Turbo => "gpt-4-turbo-preview",
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Self::Gpt4Omni => "gpt-4o",
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Self::Gpt4OmniMini => "gpt-4o-mini",
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Self::Claude3_5Sonnet => "claude-3-5-sonnet",
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Self::Claude3Opus => "claude-3-opus",
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Self::Claude3Sonnet => "claude-3-sonnet",
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Self::Claude3Haiku => "claude-3-haiku",
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Self::Gemini15Pro => "gemini-1.5-pro",
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Self::Gemini15Flash => "gemini-1.5-flash",
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Self::Custom(id) => id,
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}
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}
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pub fn display_name(&self) -> &str {
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match self {
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Self::Gpt3Point5Turbo => "GPT 3.5 Turbo",
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Self::Gpt4 => "GPT 4",
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Self::Gpt4Turbo => "GPT 4 Turbo",
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Self::Gpt4Omni => "GPT 4 Omni",
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Self::Gpt4OmniMini => "GPT 4 Omni Mini",
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Self::Claude3_5Sonnet => "Claude 3.5 Sonnet",
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Self::Claude3Opus => "Claude 3 Opus",
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Self::Claude3Sonnet => "Claude 3 Sonnet",
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Self::Claude3Haiku => "Claude 3 Haiku",
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Self::Gemini15Pro => "Gemini 1.5 Pro",
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Self::Gemini15Flash => "Gemini 1.5 Flash",
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Self::Custom(id) => id.as_str(),
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}
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}
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pub fn max_token_count(&self) -> usize {
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match self {
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Self::Gpt3Point5Turbo => 2048,
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Self::Gpt4 => 4096,
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Self::Gpt4Turbo | Self::Gpt4Omni => 128000,
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Self::Gpt4OmniMini => 128000,
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Self::Claude3_5Sonnet
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| Self::Claude3Opus
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| Self::Claude3Sonnet
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| Self::Claude3Haiku => 200000,
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Self::Gemini15Pro => 128000,
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Self::Gemini15Flash => 32000,
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Self::Custom(_) => 4096, // TODO: Make this configurable
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}
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}
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pub fn preprocess_request(&self, request: &mut LanguageModelRequest) {
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match self {
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Self::Claude3Opus | Self::Claude3Sonnet | Self::Claude3Haiku => {
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request.preprocess_anthropic()
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}
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_ => {}
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}
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}
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}
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@@ -0,0 +1,60 @@
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pub mod cloud_model;
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pub use anthropic::Model as AnthropicModel;
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pub use cloud_model::*;
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pub use ollama::Model as OllamaModel;
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pub use open_ai::Model as OpenAiModel;
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use serde::{Deserialize, Serialize};
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#[derive(Clone, Debug, Serialize, Deserialize, PartialEq)]
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pub enum LanguageModel {
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Cloud(CloudModel),
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OpenAi(OpenAiModel),
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Anthropic(AnthropicModel),
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Ollama(OllamaModel),
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}
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impl Default for LanguageModel {
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fn default() -> Self {
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LanguageModel::Cloud(CloudModel::default())
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}
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}
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impl LanguageModel {
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pub fn telemetry_id(&self) -> String {
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match self {
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LanguageModel::OpenAi(model) => format!("openai/{}", model.id()),
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LanguageModel::Anthropic(model) => format!("anthropic/{}", model.id()),
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LanguageModel::Cloud(model) => format!("zed.dev/{}", model.id()),
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LanguageModel::Ollama(model) => format!("ollama/{}", model.id()),
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}
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}
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pub fn display_name(&self) -> String {
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match self {
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LanguageModel::OpenAi(model) => model.display_name().into(),
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LanguageModel::Anthropic(model) => model.display_name().into(),
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LanguageModel::Cloud(model) => model.display_name().into(),
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LanguageModel::Ollama(model) => model.display_name().into(),
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}
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}
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pub fn max_token_count(&self) -> usize {
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match self {
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LanguageModel::OpenAi(model) => model.max_token_count(),
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LanguageModel::Anthropic(model) => model.max_token_count(),
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LanguageModel::Cloud(model) => model.max_token_count(),
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LanguageModel::Ollama(model) => model.max_token_count(),
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}
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}
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pub fn id(&self) -> &str {
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match self {
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LanguageModel::OpenAi(model) => model.id(),
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LanguageModel::Anthropic(model) => model.id(),
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LanguageModel::Cloud(model) => model.id(),
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LanguageModel::Ollama(model) => model.id(),
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}
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}
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}
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