mirror of
https://github.com/mudler/LocalAI.git
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115 lines
3.5 KiB
Go
115 lines
3.5 KiB
Go
package functions
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import (
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"encoding/json"
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"regexp"
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"github.com/go-skynet/LocalAI/pkg/utils"
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"github.com/rs/zerolog/log"
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)
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type FunctionsConfig struct {
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DisableNoAction bool `yaml:"disable_no_action"`
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NoActionFunctionName string `yaml:"no_action_function_name"`
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NoActionDescriptionName string `yaml:"no_action_description_name"`
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ParallelCalls bool `yaml:"parallel_calls"`
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NoGrammar bool `yaml:"no_grammar"`
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ResponseRegex string `yaml:"response_regex"`
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}
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type FuncCallResults struct {
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Name string
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Arguments string
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}
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func ParseFunctionCall(llmresult string, functionConfig FunctionsConfig) []FuncCallResults {
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multipleResults := functionConfig.ParallelCalls
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useGrammars := !functionConfig.NoGrammar
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results := []FuncCallResults{}
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// if no grammar is used, we have to extract function and arguments from the result
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if !useGrammars {
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// the response is a string that we have to parse
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// We use named regexes here to extract the function name and arguments
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// obviously, this expects the LLM to be stable and return correctly formatted JSON
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// TODO: optimize this and pre-compile it
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var respRegex = regexp.MustCompile(functionConfig.ResponseRegex)
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match := respRegex.FindStringSubmatch(llmresult)
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result := make(map[string]string)
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for i, name := range respRegex.SubexpNames() {
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if i != 0 && name != "" && len(match) > i {
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result[name] = match[i]
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}
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}
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// TODO: open point about multiple results and/or mixed with chat messages
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// This is not handled as for now, we only expect one function call per response
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functionName := result["function"]
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if functionName == "" {
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return results
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}
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return append(results, FuncCallResults{Name: result["function"], Arguments: result["arguments"]})
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}
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// with grammars
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// TODO: use generics to avoid this code duplication
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if multipleResults {
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ss := []map[string]interface{}{}
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s := utils.EscapeNewLines(llmresult)
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err := json.Unmarshal([]byte(s), &ss)
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if err != nil {
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log.Error().Err(err).Str("escapedLLMResult", s).Msg("multiple results: unable to unmarshal llm result")
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}
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log.Debug().Msgf("Function return: %s %+v", s, ss)
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for _, s := range ss {
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func_name, ok := s["function"]
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if !ok {
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continue
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}
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args, ok := s["arguments"]
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if !ok {
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continue
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}
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d, _ := json.Marshal(args)
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funcName, ok := func_name.(string)
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if !ok {
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continue
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}
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results = append(results, FuncCallResults{Name: funcName, Arguments: string(d)})
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}
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} else {
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// As we have to change the result before processing, we can't stream the answer token-by-token (yet?)
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ss := map[string]interface{}{}
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// This prevent newlines to break JSON parsing for clients
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s := utils.EscapeNewLines(llmresult)
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err := json.Unmarshal([]byte(s), &ss)
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if err != nil {
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log.Error().Err(err).Str("escapedLLMResult", s).Msg("unable to unmarshal llm result")
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}
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log.Debug().Msgf("Function return: %s %+v", s, ss)
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// The grammar defines the function name as "function", while OpenAI returns "name"
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func_name, ok := ss["function"]
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if !ok {
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return results
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}
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// Similarly, while here arguments is a map[string]interface{}, OpenAI actually want a stringified object
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args, ok := ss["arguments"] // arguments needs to be a string, but we return an object from the grammar result (TODO: fix)
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if !ok {
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return results
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}
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d, _ := json.Marshal(args)
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funcName, ok := func_name.(string)
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if !ok {
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return results
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}
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results = append(results, FuncCallResults{Name: funcName, Arguments: string(d)})
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}
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return results
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}
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